Assessing green manure impact on wheat productivity through Bayesian analysis of yield monitor data.

Agronomy research traditionally relies on small, controlled trial plots, which may not accurately represent the complexities and variabilities found in larger, real-world settings. To address this gap, we introduce a Bayesian methodology for the analysis of yield monitor data, systematically collected across extensive agricultural landscapes during the 2020/21 and 2021/22 growing seasons. Utilizing advanced yield monitoring equipment, our method provides a detailed examination of the effects of green manure on wheat yields in a real-world context. The results from this comprehensive analysis reveal significant insights into the impact of green manure application on wheat production, demonstrating enhanced yield outcomes across varied landscapes. This evidence suggests that the Bayesian approach to analyzing yield monitor data can offer more precise and contextually relevant information than traditional experimental designs. This research underscores the value of integrating large-scale data analysis techniques in agronomy, moving beyond small-scale trials to offer a broader, more accurate perspective on agricultural practices. The adoption of such methodologies promises to refine farming strategies and policies, ultimately leading to more effective and sustainable agricultural outcomes. The inclusion of a Python script in the appendix illustrates our analytical process, providing a tangible resource for replicating and extending this research within the agronomic community.

Genome-wide genotyping data renew knowledge on genetic diversity of a worldwide alfalfa collection and give insights on genetic control of phenology traits.

China's and Europe's dependence on imported protein is a threat to the food self-sufficiency of these regions. It could be solved by growing more legumes, including alfalfa that is the highest protein producer under temperate climate. To create productive and high-value varieties, the use of large genetic diversity combined with genomic evaluation could improve current breeding programs. To study alfalfa diversity, we have used a set of 395 alfalfa accessions (i.e. populations), mainly from Europe, North and South America and China, with fall dormancy ranging from 3 to 7 on a scale of 11. Five breeders provided materials (617 accessions) that were compared to the 400 accessions. All accessions were genotyped using Genotyping-by-Sequencing (GBS) to obtain SNP allele frequency. These genomic data were used to describe genetic diversity and identify genetic groups. The accessions were phenotyped for phenology traits (fall dormancy and flowering date) at two locations (Lusignan in France, Novi Sad in Serbia) from 2018 to 2021. The QTL were detected by a Multi-Locus Mixed Model (mlmm). Subsequently, the quality of the genomic prediction for each trait was assessed. Cross-validation was used to assess the quality of prediction by testing GBLUP, Bayesian Ridge Regression (BRR), and Bayesian Lasso methods. A genetic structure with seven groups was found. Most of these groups were related to the geographical origin of the accessions and showed that European and American material is genetically distinct from Chinese material. Several QTL associated with fall dormancy were found and most of these were linked to genes. In our study, the infinitesimal methods showed a higher prediction quality than the Bayesian Lasso, and the genomic prediction achieved high (>0.75) predicting abilities in some cases. Our results are encouraging for alfalfa breeding by showing that it is possible to achieve high genomic prediction quality.

Dynamics of Maize Vegetative Growth and Drought Adaptability Using Image-Based Phenotyping Under Controlled Conditions.

Changes in climate are likely to have a negative impact on water availability and soil fertility in many maize-growing agricultural areas. The development of high-throughput phenotyping platforms provides a new prospect for dissecting the dynamic complex plant traits such as abiotic stress tolerance into simple components. The growth phenotypes of 20 maize (Zea mays L.) inbred lines were monitored in a non-invasive way under control, nitrogen, and water limitation as well as under combined nitrogen and water stress using an automated phenotyping system in greenhouse conditions. Thirteen biomass-related and morphophysiological traits were extracted from RGB images acquired at 33 time points covering developmental stages from leaf count 5 at the first imaging date to leaf count 10-13 at the final harvest. For these traits, genetic differences were identified and dynamic developmental trends during different maize growth stages were analyzed. The difference between control and water stress was detectable 3-10 days after the beginning of stress depending on the genotype, while the effect of limited nitrogen supply only induced subtle phenotypic effects. Phenotypic traits showed different response dynamics as well as multiple and changing interaction patterns with stress progression. The estimated biovolume, leaf area index, and color ratios were found to be stress-responsive at different stages of drought stress progression and thereby represent valuable reference indicators in the selection of drought-adaptive genotypes. Furthermore, genotypes could be grouped according to two typical growth dynamic patterns in water stress treatments by c-means clustering analysis. Inbred lines with high drought adaptability across time and development were identified and could serve as a basis for designing novel genotypes with desired, stage-specific growth phenotypes under water stress through pyramiding. Drought recovery potential may play an equal role as drought tolerance in plant drought adaptation.

Chemical Characterization and Antibacterial Activity of Essential Oil of Medicinal Plants from Eastern Serbia.

The objective of this study was to evaluate wild growing Satureja kitaibelii, Thymus serpyllum, Origanum vulgare, Achillea millefolium and Achillea clypeolata with respect to their essential oil (EO) content, composition and antimicrobial activity. The five species were collected at Mt. Rtanj and the village of Sesalac, Eastern Serbia. The main EO constituents of Lamiaceae plants were p-cymene (24.4%), geraniol (63.4%) and germacrene D (21.5%) in Satureja kitaibelii, Thymus serpyllum and Origanum vulgare ssp. vulgare, respectively. A. millefolium EO had multiple constituents with major ones being camphor (9.8%), caryophyllene oxide (6.5%), terpinen-4-ol (6.3%) and 1,8-cineole (5.6%), while the main EO constituents of A. clypeolata were 1,8-cineole (45.1%) and camphor (18.2%). Antimicrobial testing of the EO showed that Staphylococcus aureus (Gram-positive) was more sensitive to all of the tested EOs than Escherichia coli (Gram-negative). S. kitaibelii EO showed the highest antimicrobial activity against both tested bacterial strains. This is the first study to characterize the EO composition and antimicrobial activity of these five medicinal species from Eastern Serbia in comparison with comprehensive literature data. The results can be utilized by the perfumery, cosmetics, food and pharmaceutical industries, but also for healing purposes in self-medication.

Image-Derived Traits Related to Mid-Season Growth Performance of Maize Under Nitrogen and Water Stress.

Phenotypic measurements under controlled cultivation conditions are essential to gain a mechanistic understanding of plant responses to environmental impacts and thus for knowledge-based improvement of their performance under natural field conditions. Twenty maize inbred lines (ILs) were phenotyped in response to two levels of water and nitrogen supply (control and stress) and combined nitrogen and water deficit. Over a course of 5 weeks (from about 4-leaf stage to the beginning of the reproductive stage), maize phenology and growth were monitored by using a high-throughput phenotyping platform for daily acquisition of images in different spectral ranges. The focus of the present study is on the measurements taken at the time of maximum water stress (for traits that reflect plant physiological properties) and at the end of the experiment (for traits that reflect plant architectural and biomass-related traits). Twenty-five phenotypic traits extracted from the digital image data that support biological interpretation of plant growth were selected for their predictive value for mid-season shoot biomass accumulation. Measured fresh and dry weights after harvest were used to calculate various indices (water-use efficiency, physiological nitrogen-use efficiency, specific plant weight) and to establish correlations with image-derived phenotypic features. Also, score indices based on dry weight were used to identify contrasting ILs in terms of productivity and tolerance to stress, and their means for image-derived and manually measured traits were compared. Color-related traits appear to be indicative of plant performance and photosystem II operating efficiency might be an importance physiological parameter of biomass accumulation, particularly under severe stress conditions. Also, genotypes showing greater leaf area may be better adapted to abiotic stress conditions.

Estimation of genetic diversity and relationship in sugar beet pollinators based on SSR markers

Background: Genetic diversity studies are important for the selection of parents with a greater combination capacity that, when crossed, increase the chances of obtaining superior genotypes. Thus, 26 polymorphic simple sequence repeat (SSR) primers were used to assess the genetic diversity of 140 individual samples from 12 diploid sugar beet pollinators (pollen parents) and two cytoplasmic male sterile (cms) lines (seed parents). Eight pollinators originated from three research centers in the United States Department of Agriculture, while four pollinators and cms lines were from the Institute of Field and Vegetable Crops, Novi Sad, Serbia. Results: In total, 129 alleles were obtained, with a mean of 3.2 alleles per SSR marker. The observed heterozygosity ranged from 0.00 to 0.87 (mean = 0.30). Expected heterozygosity and Shannon's information index were the lowest for marker BQ590934 and the highest for markers SB15s and FDSB502s; the same markers were the most informative, with PIC values of 0.70 and 0.69, respectively. Three private alleles were found in pollinator EL0204; two in pollinator C51; and one in pollinators NS1, FC221, and C93035. Molecular variance showed that 77.34% of the total genetic variation was attributed to intrapopulation variability. Cluster and correspondence analysis grouped sugar beet pollinators according to the breeding centers, with few exceptions, which indicate that certain amount of germplasm was shared, although centers had their own breeding programs. Conclusions: The results indicate that this approach can improve the selection of pollinators as suitable parental components and could further be applied in sugar beet breeding programs.

Analysis of oilseed rape stem weevil chemical control using a damage rating scale.

BACKGROUND: Rape stem weevil (Ceutorhynchus napi Gyll.) and cabbage stem weevil (C. pallidactylus Marsh.) can cause significant yield losses to oilseed rape (Brassica napus L.), and chemical control is often needed to protect crops from these pests. The efficacy of six insecticides, chlorpyriphos + cypermethrin, bifenthrin, alpha-cypermethrin, pirimiphos-methyl, thiacloprid and tau-fluvalinate, was tested in a 4 year field trial. Besides the standard efficacy analysis expressed through the number of larvae per stem, a damage rating scale was introduced and modelled using a regression model for ordinal categorical data. RESULTS: Compared with the control, expressed through damage rating and larval number, treatments with chlorpyriphos + cypermethrin and bifenthrin, showed higher efficacy in the control of stem weevils compared with alpha-cypermethrin and pirimiphos-methyl. The lowest efficacy was observed in treatments with tau-fluvalinate and thiacloprid. CONCLUSION: This study showed that a combined efficacy evaluation expressed through both damage rating scale and the count of larvae, supported by an ordinal regression model for data analysis, is indispensable for obtaining accurate results. © 2017 Society of Chemical Industry.

Population structure in a wheat core collection and genomic loci associated with yield under contrasting environments.

A set of 96 winter wheat accessions sampled from a variety of geographic origins, including cultivars and breeding lines, were characterized with 46 genome-wide SSR loci for genetic diversity and population structure. The genetic diversity within these accessions was examined using a genetic distance-based and a model-based clustering method. The model-based analysis identified an underlying population structure comprising of four distinct sub-populations which corresponded well with distance-based groupings. Information on the population structure is taken into account in an association mapping study of grain yield from a 3-years field trial incorporating fully irrigated, rainfed and drought stress treatments. A total of 21 marker-grain yield associations (P < 0.01) were identified with nine SSR markers. Most associations were detected only in one to three environments (treatment/year combination), with an average R ( 2 ) value around 13 %. However, marker gwm484 (on chromosome 2D) was associated with yield in six environments, including irrigated, rainfed and drought stress treatments, suggesting it could be used to improve grain yield across a range of environments. Variation in grain yield at this locus was associated with earliness, early vigour, kernels per spikelet and harvest index. Microsatellite locus psp3200 (on chromosome 6D) was associated with yield in dry and hot environments, which was related to earliness, early vigour, productive tillering and total biomass per plant. Partial least squares regression, with nine environmental factors, showed that precipitation from tillering to maturity was the main environmental factor causing marker × environment associations for grain yield.

Genetic and association mapping study of wheat agronomic traits under contrasting water regimes.

Genetic analyses and association mapping were performed on a winter wheat core collection of 96 accessions sampled from a variety of geographic origins. Twenty-four agronomic traits were evaluated over 3 years under fully irrigated, rainfed and drought treatments. Grain yield was the most sensitive trait to water deficit and was highly correlated with above-ground biomass per plant and number of kernels per m(2). The germplasm was structured into four subpopulations. The association of 46 SSR loci distributed throughout the wheat genome with yield and agronomic traits was analyzed using a general linear model, where subpopulation information was used to control false-positive or spurious marker-trait associations (MTAs). A total of 26, 21 and 29 significant (P < 0.001) MTAs were identified in irrigated, rainfed and drought treatments, respectively. The marker effects ranged from 14.0 to 50.8%. Combined across all treatments, 34 significant (P < 0.001) MTAs were identified with nine markers, and R(2) ranged from 14.5 to 50.2%. Marker psp3200 (6DS) and particularly gwm484 (2DS) were associated with many significant MTAs in each treatment and explained the greatest proportion of phenotypic variation. Although we were not able to recognize any marker related to grain yield under drought stress, a number of MTAs associated with developmental and agronomic traits highly correlated with grain yield under drought were identified.