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.

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.

Comparative transcriptome analyses reveal insights into catkin bloom patterns in pecan protogynous and protandrous cultivars.

In perennial plants such as pecan, once reproductive maturity is attained, there are genetic switches that are regulated and required for flower development year after year. Pecan trees are heterodichogamous with both pistillate and staminate flowers produced on the same tree. Therefore, defining genes exclusively responsible for pistillate inflorescence and staminate inflorescence (catkin) initiation is challenging at best. To understand these genetic switches and their timing, this study analyzed catkin bloom and gene expression of lateral buds collected from a protogynous (Wichita) and a protandrous (Western) pecan cultivar in summer, autumn and spring. Our data showed that pistillate flowers in the current season on the same shoot negatively impacted catkin production on the protogynous 'Wichita' cultivar. Whereas fruit production the previous year on 'Wichita' had a positive effect on catkin production on the same shoot the following year. However, fruiting the previous year nor current year pistillate flower production had no significant effect on catkin production on 'Western' (protandrous cultivar) cultivar. The RNA-Seq results present more significant differences between the fruiting and non-fruiting shoots of the 'Wichita' cultivar compared to the 'Western' cultivar, revealing the genetic signals likely responsible for catkin production. Our data presented here, indicates the genes showing expression for the initiation of both types of flowers the season before bloom.

Establishing three warm-season turfgrasses with tailored water: I. Growth, cover, and nitrate leaching losses.

Although treated effluent is being increasingly used to irrigate mature turfgrass, information on its use to establish grass is limited. Greenhouse experiments were conducted in 2015 and 2017 to examine establishment and nitrate leaching from three warm-season grasses: buffalograss [Buchloe dactyloides (Natt.) Eng.] 'SWI 2000', inland saltgrass [Distichlis spicata (L.) Greene], and bermudagrass [Cynodon dactylon (L.) Pers.] 'Princess77'. All grasses were grown with tailored (tertiary treated effluent with 15 mg L-1 of NO3 -N) water. Grasses were established from seed in a loamy sand and irrigated with either tailored or potable water plus granular Ca(NO3 )2 fertilizer. Leachate collected at 10- and 30-cm depths was analyzed for NO3 -N and electrical conductivity. Overall, establishment was faster and coverage was greater in 2015 than in 2017, but neither differed between irrigation treatments when grasses were analyzed separately. At the end of both establishment periods, bermudagrass and buffalograss coverage was generally greater than that of inland saltgrass. In 2017, bermudagrass irrigated with tailored water resulted in greater coverage than buffalograss or inland saltgrass. In 2015, nitrate concentrations were greater in leachate collected from bermudagrass and inland saltgrass irrigated with tailored water than from grasses irrigated with potable water. Nitrate concentrations in leachate were generally lower in 2017, reaching a maximum value of 65.3 mg L-1 when averaged over all treatment combinations, and did not differ between treatments. Our data suggest that the three grasses studied can be successfully established from seed using tailored waters.

Establishing three warm-season turfgrasses with tailored water: II. Root development, nitrate accumulation in plant tissue and soil, and relationship with leaching.

Greenhouse experiments were conducted in 2015 and 2017 to assess the feasibility of establishing three warm-season grasses-buffalograss [Buchloe dactyloides (Natt.) Eng.] 'SWI 2000', inland saltgrass (Distichlis spicata L.), and bermudagrass (Cynodon dactylon L.) 'Princess77'-with tailored water (tertiary treated effluent with 15 mg L-1 of NO3 -N) and to examine the impact on nitrate accumulation in soils and plant tissue and on root development. Grasses were established from seed in a loamy sand and irrigated with either tailored or potable water plus granular Ca(NO3 )2 fertilizer. Leachate collected at 10- and 30-cm depths was analyzed for NO3 -N and electrical conductivity. Root samples were collected to measure root length density (RLD) and root surface area (RSA). Weekly clippings were collected to determine total clipping yield and measure N content. Generally, there was no difference in establishment, RLD, or RSA between the two irrigation treatments. Highest RLD values were reported for bermudagrass, followed by buffalograss and inland saltgrass. Correlation analyses suggest that nitrate levels in leachate were lower in faster-growing grasses and in grasses with more extensive root systems, compared with slower-growing grasses with less roots, regardless of fertilization treatment. Total N in clippings was highest in inland saltgrass and lower in buffalograss and bermudagrass, indicating that N was limiting for faster-growing grasses. More research is needed to determine optimal N rates for establishing grasses that both optimize growth and minimize nitrate leaching.

Modeling nematode population dynamics using a multivariate poisson model with spike and slab variable selection.

Model-based learning of organism dynamics is challenging, particularly when modeling count correlated data. In this paper, we adapt the multivariate Poisson distribution to model nematode dynamics. This distribution relaxes the mean-equal-variance property of the univariate Poisson distribution and allows recovery of the correlation among nematode genera. An observational dataset with 68 soil samples, 11 nematode genera, and 12 soil parameters is analyzed. The Spike and Slab Variable Selection procedure is adapted to obtain parsimonious models for the nematode occurrence. Nematode genus to genus interaction is assessed through the correlation matrix of the model. A simulation study validated the model's implementation. As a result, the model determined the most important covariates for each nematode and classified pairs of nematodes as: sympathetic, antagonistic or neutral, based on their estimated correlations. The model is useful for researchers and practitioners interested in studying population dynamics. In particular, the current results are important inputs when planning strategies for improving or managing soil health regarding nematodes.

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.