Estimating daily minimum grass temperature to quantify frost damage to winter wheat during stem elongation in the central area of Huang-Huai plain in China

Frost damage to winter wheat during stem elongation frequently occurred in the Huang-Huai plain of China, leading to considerable yield losses. Minimum Stevenson screen temperature (ST_min) and minimum grass temperature (GT_min) have long been used to quantify frost damage. Although GT_min has higher accuracy than ST_min, it is limited in application due to the lack of data. Therefore, this study aimed to select appropriate environmental variables to estimate GT_min, as well as to quantify the frost damage. Shangqiu, a frost-prone winter wheat area in the central Huang-Hui plain, was selected as the study area. From the descriptive statistics of ST, air relative humidity (RH), wind speed (WS), cloud fraction (CF), and volumetric soil water content (VWC) during temperature decreasing and increasing, seven variables significantly correlated with GT_min were selected, including ST_min, maximum reduction of ST (RST), maximum increase of ST (IST), minimum RH during temperature increasing (RH_min), WS at ST_min occurrence (WS), minimum VWC during temperature decreasing (VWC_min), and nightly CF. Multiple linear regression (MLR), support vector regression (SVR), random forest (RF), and K-nearest neighbor (KNN) were adopted for estimating GT_min based on the various combinations of the variables. Results showed the more variables, the higher the accuracy for the MLR and SVR. However, this pattern was not always true for the KNN and RF. The KNN based on ST_min, RST, IST, RH_min, and WS achieved the highest accuracy, with R² of 0.9992, RMSE of 0.14 ℃, and MAE of 0.076 ℃. The overall classification accuracy for frost damage identified by the estimated GT_min reached 97.1% during stem elongation of winter wheat from 2017 to 2021. The integrated frost stress (IFS) index calculated by the estimated and measured GT_min maintained high linear fitting accuracy. The KNN with fewer variables demonstrated good applicability at the regional scale.

Leveraging breeding programs and genomic data in Norway spruce (Picea abies L. Karst) for GWAS analysis

BACKGROUND

Genome-wide association studies (GWAS) identify loci underlying the variation of complex traits. One of the main limitations of GWAS is the availability of reliable phenotypic data, particularly for long-lived tree species. Although an extensive amount of phenotypic data already exists in breeding programs, accounting for its high heterogeneity is a great challenge. We combine spatial and factor-analytics analyses to standardize the heterogeneous data from 120 field experiments of 483,424 progenies of Norway spruce to implement the largest reported GWAS for trees using 134 605 SNPs from exome sequencing of 5056 parental trees.

RESULTS

We identify 55 novel quantitative trait loci (QTLs) that are associated with phenotypic variation. The largest number of QTLs is associated with the budburst stage, followed by diameter at breast height, wood quality, and frost damage. Two QTLs with the largest effect have a pleiotropic effect for budburst stage, frost damage, and diameter and are associated with MAP3K genes. Genotype data called from exome capture, recently developed SNP array and gene expression data indirectly support this discovery.

CONCLUSION

Several important QTLs associated with growth and frost damage have been verified in several southern and northern progeny plantations, indicating that these loci can be used in QTL-assisted genomic selection. Our study also demonstrates that existing heterogeneous phenotypic data from breeding programs, collected over several decades, is an important source for GWAS and that such integration into GWAS should be a major area of inquiry in the future.

Can intensive silvicultural management minimize the effects of frost on restoration plantations in subtropical regions?

Temperature is one of the main factors that influence field establishment of forest species. In high-altitude tropical regions and in subtropical regions, the occurrence of frost represents an important restriction in the trajectory and continuity of ecological processes. Thus, we aimed to characterize frost damage in nine native forest species under different silvicultural management schemes in plantations for the restoration of a riparian forest area in southern Brazil. The experiment was carried out in the Quarta Colônia State Park, and seedlings of nine native tree species, representing the Subtropical Seasonal Forest. Frost damage was measured using a visual damage scale based on the frost damage experienced in the winter of 2016. In addition, to evaluate the resilience of the species, height and diameter data were collected over the duration of the experiment. The species Solanum mauritianum was classified as frost resistant; therefore, we propose that it should be recommended for cultivation in regions where frost events usually occur. The other species studied, both the pioneers, S. terebinthifolius, Enterolobium contortisiliquum, Ceiba speciosa, and Inga marginata, as well as the secondary ones, Actinostemon concolor, Trichilia elegans, T. claussenii, and Eugenia rostrifolia, were influenced by the silvicultural management schemes used. Plants managed under intensive silviculture showed lower levels of frost damage and higher survival rates.

Hyperspectral imaging technology combined with deep forest model to identify frost-damaged rice seeds

In recent years, deep learning models have been widely used in the field of hyperspectral imaging. However, the training of deep learning models requires not only a large number of samples, but also the need to set too many hyper-parameters, which is time consuming and laborious for researchers. This study used hyperspectral imaging technology combined with a deep learning model suitable for small-scale sample data sets, deep forests (DF) model, to classify rice seeds with different degrees of frost damage. During the period, three spectral preprocessing methods (Savitzky-Golay first derivative (SG1), standard normal variate (SNV), and multivariate scatter correction (MSC)) were used to process the original spectral data, and three feature extraction algorithms (principal component analysis (PCA), successive projections algorithm (SPA), and neighborhood component analysis (NCA)) were used to extract the characteristic wavelengths. Moreover, DF model and three traditional machine learning models (decision tree (DT), k-nearest neighbor (KNN), and support vector machine (SVM)) were built based on different numbers of sample sets. After multivariate data analysis, it showed that the pretreatment effect of MSC was the most excellent, and the characteristic wavelength extracted by NCA algorithm was the most useful. In addition, the performance of DF model was better than these three traditional classifier models, and it still performed well in small-scale sample set data. Therefore, DF model was chosen as the best classification model. The results of this study show that the DF model maintains good classification performance in small-scale sample set data, and it has a good application prospect in hyperspectral imaging technology.

Ground level air convection produces frost damage patterns in turfgrass

Frost injury patterns are commonly observed on the warm-season turfgrass species bermudagrass (Cynodon species Rich.), zoysiagrass (Zoysia species Willd.), and buffalograss [Bouteloua dactyloides (Nutt.) J.T. Columbus] in cool-temperate and subtropical zones. Qualitative observations of these injury patterns are presented and discussed. A model for the formation of such patterns based on thermal instability and convection of air is presented. The characteristic length scale of the observed frost pattern injury requires a temperature profile that decreases with height from the soil to the turfgrass canopy surface followed by an increase in temperature with height above the turfgrass canopy. This is justified by extending the earth temperature theory to include a turf layer with atmosphere above it. Then the theory for a thermally unstable layer beneath a stable region by Ogura and Kondo is adapted to a turf layer to include different parameter values for pure air, as well as for turf, which is treated as a porous medium. The earlier porous medium model of Thompson and Daniels proposed to explain frost injury patterns is modified to give reasonable agreement with observed patterns.

Frost decreases content of sugars, ascorbic acid and some quercetin glycosides but stimulates selected carotenes in Rosa canina hips

Primary and secondary metabolites of Rosa canina hips were determined by HPLC/MS during ripening and after frost damage. Rose hips were harvested six times from the beginning of September until the beginning of December. Color parameters a*, b* and L* decreased during maturation. Glucose and fructose were the predominant sugars representing up to 92% total sugars, and citric acid was the major organic acid detected in rose hips (constituting up to 58% total organic acids). Total sugar and ascorbic acid content significantly decreased after frost damage; from 42.2 to 25.9 g 100 g(-1) DW for sugars and from 716.8 to 176.0 mg 100 g(-1) DW for ascorbic acid. Conversely, β-carotene and lycopene levels increased in frostbitten rose hips to 22.1 and 113.2 mg 100 g(-1) DW, respectively. In addition to cyanidin-3-glucoside (highest level in hips was 125.7 μg 100 g (-1) DW), 45 different phenolic compounds have been identified. The most abundant were proanthocyanidins (their levels amounted up to 90% of total flavanol content) and their content showed no significant differences during maturation. The levels of catechin, phloridzin, flavanones and several quercetin glycosides were highest on the first three sampling dates and decreased after frost. Antioxidant capacity similarly decreased in frostbitten rose hips. Total phenolic content increased until the third sampling and decreased on later samplings.

Quantification of frost damage in plant tissues by rates of electrolyte leakage

A method for assessing frost hardiness of plant tissues [using shoots of Picea rubens Sarg. syn P. rubra (Du Roi) Link] has been developed based upon the rate of electrolyte leakage from shoots immersed in distilled water after exposure to a range of freezing treatments. The relationship between conductivity (the electrolyte concentration in solution) and time has been shown to follow an asymptotic curve, which may be represented by a first-order equation: C_t -C_o = C_auto -C_o - C_-kt ) where C₁ is the conductivity at time t, C_o is the initial conductivity, C_auto is the conductivity after autoclaving and k is the first-order rate constant (units time^-1 ). The rate of electrolyte leakage (k) varies directly with the extent of tissue damage. In P. rebens a rate of 0-4%, h^-1 distinguished between shoots which eventually died, and shoots which remained alive. A minimum of 3 conductivity measurements (after 1 day, 5 days and after autoclaving) is required for a reliable estimate of k. This objective, quantitative method of assessing frost hardiness may therefore be used directly to estimate LT₅₀ values within a population.