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Abstract 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.

Abstract The desert Southwest is the oldest area of continuous crop production in the continental USA. Tepary beans (Phaseolus acutifolius A. Gray) were a part of the ancient, irrigated cropping systems, but they were nearly lost during the twentieth century. They are now the subject of heightened research and breeding activity. We wished to ascertain the field performance of varieties improved by selection within the region with those selected outside the region. In one study, the selected varieties were evaluated in two locations across two seasons, and in another, they were evaluated at three locations in the Rio Grande Valley, the principal agricultural production region in New Mexico. A collection of 21 tepary landraces was also examined at the same three locations in the Rio Grande River Valley of New Mexico. Both selected and landrace varieties demonstrated a production capacity above 2000 kg ha−1 under irrigated conditions. Seed size (as determined by 100‐seed weights) was also examined in the selected varieties. Two varieties (TARS Tep32 and CSU148) displayed seed size at the lower range of the small market class of common beans. The developmental phenology of the selected varieties was similar across two seasons. One selected variety, CSU148, also displayed a high level of resistance to powdery mildew during one season favorable for disease development. The other selected varieties exhibited intermediate host‐resistance responses to infection.

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.

Lutescens, or lutescent, plant mutants produce leaves that are abnormally light yellow-green compared with normal plants, and are observed in multiple species of Capsicum as well as other genera such as Zea, Oryza, and Oenothera. Previous investigations into the lutescent phenotype in Capsicum have focused on genetic and transcriptomic analyses, and comparatively little is known about the phytochemical constituents of the lutescent leaf phenotype. Previous research in similar lutescent mutants in Capsicum and Oryza species has attributed their pale yellow-green leaf color and poor vigor to deficient chloroplast development. A total of 25 accessions of Capsicum lutescens mutants were phenotyped and analyzed based on a multivariate approach, using ‘Jupiter’ bell pepper (Capsicum annuum) with normal green leaves as a contextual benchmark. Photosynthetic pigments from mutant leaves were extracted and analyzed using high-performance liquid chromatography (HPLC); reflectance of the leaf material was measured with a chromameter using the L*a*b* color space. The chlorophyll a (Chl a)/b (Chl b) ratio was greater in leaves of lutescens mutants than in ‘Jupiter’. Multivariate statistical analyses revealed all lutescent mutant accessions could be distinguished from the ‘Jupiter’ contextual benchmark by variables indicating poor chloroplast development and increased photooxidative stress in lutescent mutant accessions. The lutescent leaf phenotype was not found to be caused by elevated xanthophyll or decreased chlorophyll concentrations. Furthermore, multivariate analysis revealed the lutescent mutant phenotype to be variable, with a wide range of phenotypes clustered into four major groups.

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.

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.

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.

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.

Copyright of Colombian Journal of Statistics / Revista Colombiana de Estadística is the property of Universidad Nacional de Colombia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A compost program was developed on-farm, utilizing tree trimming biomass from a commercial pecan farm comprised of 14-year-old improved cultivar Western Schley pecan (Carya illinoinensis) tree stands. The direct soil application of shredded pecan tree biomass (P) and dairy manure (M) served as a standard on-farm practice. Three composts were produced using P and M with varying levels of other inputs and processing. The PM compost contained only P and M and its production included only weekly turning and watering. The other two composts included P, M, unfinished compost, and clay inputs, and either additional landscaping residues (A) (designated PM/A compost) or "green chop" (on-farm grown legumes, G) (designated PMG/A compost); production of PM/A and PMG/A composts included additional processing steps intended to improve compost quality per the recommendations of a compost consulting company. Soil samples were taken at three depths (0–15 cm, 15–30 cm, 30–61 cm) in November 2017 from the 1.3 ha study plot of trees. The standard practice and compost treatments were applied at approximately 18 t/ha in January 2018 and 2019 at a 15 cm depth. Soils were re-sampled at the end of the two-year study. Composts and soils were analyzed for: pH, sodium adsorption ratio (SAR), electrical conductivity, and total carbon, organic matter, magnesium, calcium, sodium, nitrate-N, total Kjeldahl nitrogen (TKN), available phosphorus, potassium, zinc, manganese, iron, and copper contents. Pecan tree leaf nutrient content, stem water potential, and leaf greenness were also measured one and two years after soil amendment application. While increases in several soil properties were observed with the treatments, only available phosphorus content was significantly different between pre and post at all depths. Electrical conductivity, TKN, Fe, Cu, SAR, and Na content showed significant differences in the upper soil layers. No differences in leaf properties were observed. This suggests that there are minimal differences in the outcomes for compost application compared to in-situ biomass application; additional compost inputs and processing did not provide additional short-term soil or plant benefits for pecan tree production. More work is needed to determine if there are long-term benefits to soil quality, plant health and performance, or carbon sequestration that impact the economic and environmental decision-making processes for composting and application of local organic wastes. [ABSTRACT FROM AUTHOR]

Wine aroma and flavor profiles of petit manseng associated with various vineyard nitrogen treatments were evaluated using descriptive sensory analysis. Treatments consisted of six field replicates of (1) control with no nitrogen or sulfur applications, (2) 30 kg/ha of nitrogen (calcium nitrate) applied to soil just after flowering, (3) 15 kg/ha of nitrogen (urea) in two split foliar applications prior to véraison, and (4) 15 kg/ha of nitrogen (urea) and 5 kg/ha of sulfur (micronized sulfur) in two split foliar applications prior to véraison. Eight panelists were trained to identify and quantify a total of 24 wine aroma, flavor, texture/mouthfeel and aftertaste attributes. Analysis of variance (ANOVA) revealed significant variation in 23 of the 24 attributes used among the four treatments. Biplots of principal components analysis of wine flavor and aroma suggest that foliar N and foliar NS treatments gave more tropical flavors and aromas compared to other treatments. Sensory aspects that characterized the aroma and flavor profile of the different petit manseng wine may be correlated with chemical changes found in the fruit and wine as a result of vineyard treatment. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Copyright of Glasnik Zastite Bilja is the property of Zadruzna Stampa D.D. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: In this study, we assessed college students' eating behaviors and determined the association between their eating behaviors and body image perception (feelings about body, shape, and weight). Methods: We surveyed college students (N = 184) enrolled at least parttime and actively living on campus during the 2019-2020 academic year. We used 4 validated surveys (EDE-Q, NEMS-P, SATAQ-3 and SATAQ-4) to collect all data. Two trained facilitators aided students in completing the surveys in the campus's nutrition lab. We used weighted linear regression to assess the association between eating behaviors (frequency of fruit, vegetables, candy, and chips consumption available in dorm rooms, and meal consumption frequency at restaurants) (independent variables) and body image perception (dependent variable). We also calculated odds ratios. Results: We found statistically significant associations between students reducing their consumption of available fruit, vegetables, candy, soda, and chips in their dorm rooms to control weight and shape, choosing meals more frequently at fast-food versus sit-down restaurants, and unhappiness with weight and shape. Conclusion: Our results may assist college campus personnel in providing students with the necessary skills to develop better eating habits and improved body image. Students with a positive body image and a healthy relationship with food are more likely to have better mental and physical health. [ABSTRACT FROM AUTHOR]

Limited information is available on the optimal frequency and amounts of irrigation water needed to establish cool‐season turfgrasses from seed in arid environments. A 2 × 2 factorial study was conducted at New Mexico State University in 2012 and repeated in 2017 to investigate the effect of four irrigation treatments: two reference evapotranspiration (ETo) rates (60 and 120%), and two frequencies (daily and every other day [EOD]) on the establishment of several varieties of Kentucky bluegrass (Poa pratensis L.), perennial ryegrass (Lolium perenne L.), and tall fescue [Schedonorus arundinaceus (Schreb.) Dumort] (TF). Our hypothesis was that irrigation rates below 100% ETo replacement applied EOD would negatively affect turfgrass establishment. Turfgrasses were seeded in the fall and digital image analysis was used to estimate percent coverage. The Area Under the Curve model was used to estimate days after seeding needed to reach 50 (DAS50) and 90% coverage (DAS90). Statistical analysis indicated a significant interaction (p <.0001) between ETo and species and that irrigation frequency did affect establishment. Perennial ryegrass established the fastest and DAS90 was not affected by irrigation treatment. Kentucky bluegrass and TF established faster when irrigated daily at 120% ETo. Varieties within the same species all performed equally. In general, our study suggests that cool‐season turfgrasses can be established in arid regions using water conserving measures such as irrigating EOD at rates below 100% ETo replacement. Further studies are needed to determine lowest ETo replacement levels or irrigation frequency possible to achieve establishment. Core Ideas: Final establishment of cool‐season turfgrasses was not affected by irrigation frequency and grasses can potentially be irrigated EOD.Perennial ryegrass established more rapidly than Kentucky bluegrass and tall fescue with irrigation as low as 60% ETo.Fastest establishment of Kentucky bluegrass and tall fescue was achieved when 120% ETo was applied daily. [ABSTRACT FROM AUTHOR]

The southwestern United States–northern Mexico Borderland Region (SW) is an arid to semi‐arid region characterized by high temperatures, drought, and frequently saline soil conditions. Nonetheless, maize (Zea mays) has been cultivated in the region for several millennia and recently has increasingly contributed to a growing dairy industry. Water resources are declining, and saline or brackish irrigation water is an increasing problem in the SW. Irrigation water that is highly saline can cause a buildup of salts in the soil. Adapted SW maize germplasm may offer stress tolerance that could be incorporated into breeding programs. The objective of this study was to evaluate 13 maize cultivars and compare them to four commercial hybrids across 2 yr and three locations in New Mexico. Two of the locations were characterized by saline and/or brackish conditions. Agronomic factors including forage yield, forage quality, survival rates, and plant height were examined. The landrace 'Mexican June' and the synthetic population Arizona 8601 demonstrated good forage yields and higher survival rates at one stress location during 2016 (p <.05). Most southwestern landraces showed lower forage quality when compared to silage‐specific commercial hybrids. The landrace 'Montgomery County Blue and White' was an exception; it produced forage quality similar to that of one of the commercial hybrids while also producing comparable yield. Results indicate there is potential for further development of these genetic resources for selection of improved salinity tolerance in forage maize for production in semi‐arid regions. Core Ideas: Increasing salinity of agricultural soils in the western United States affects forage maize production.Southwestern maize genetic resources may offer salinity tolerance for varietal improvement.Southwestern landraces and a synthetic population demonstrated good forage yield at a stress location.With one exception, Southwestern maize germplasm did not demonstrate good forage nutritional quality. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]