Path and ridge regression analysis of seed yield and seed yield components of Russian wildrye (Psathyrostachys juncea Nevski) under field conditions

Comparative analysis of different Karnal bunt disease prediction models developed by machine learning techniques for Punjab conditions

Timely prediction of pathogen is important key factor to reduce the quality and yield losses. Wheat is major crop in northern part of India. In Punjab, wheat face challenge by different diseases so the study was conducted for two locations viz. Ludhiana and Bathinda. The information regarding the occurrence of Karnal bunt in 12 consecutive crop seasons (from 2009-10 to 2020-21) in Ludhiana district and in 9 crop seasons (from 2010-11 to 2018-19) in Bathinda district, was collected from the Wheat Section of the Department of Plant Breeding and Genetics at Punjab Agricultural University (PAU), located in Ludhiana. The study aims to investigate the adequacy of various methods of machine learning for prediction of Karnal bunt using meteorological data for different time period viz. February, March, 15 February to 15 March and overall period obtained from Department of Climate Change and Agricultural Meteorology, PAU, Ludhiana. The most intriguing outcome is that for each period, different disease prediction models performed well. The random forest regression (RF) for February month, support vector regression (SVR) for March month, SVR and BLASSO for 15 February to 15 March period and random forest for overall period surpassed the performance than other models. The Taylor diagram was created to assess the effectiveness of intricate models by comparing various metrics such as root mean square error (RMSE), root relative square error (RRSE), correlation coefficient (r), relative mean absolute error (MAE), modified D-index, and modified NSE. It allows for a comprehensive evaluation of these models' performance.

Association Analysis of Tiller-Related Traits with EST-SSR Markers in Psathyrostachys juncea

Psathyrostachys juncea is a long-lived perennial Gramineae grass with dense basal tillers and soft leaves. It is used widely in cold and dry areas of Eurasia and North America to establish grazing pasture and is even used as an ideal plant for revegetation and ecological restoration. Plant architecture, especially tillering traits, is critical for bunch grasses in breeding programs, and these traits in plants are mostly quantitative traits. In this study, the genetic diversity, population structure, and linkage disequilibrium of 480 individual lines were analyzed using 127 pairs of the EST-SSR marker, and a significant association between ten plant-architecture-related traits of P. juncea and molecular markers was found. The results of the genetic diversity analysis showed that the number of observed alleles was 1.957, the number of effective alleles was 1.682, Shannon's information index was 0.554, observed heterozygosity was 0.353, expected heterozygosity was 0.379, and the polymorphism information content was 0.300. A total of 480 individual lines were clustered into five groups based on population genetic structure, principal coordinate analysis, and unweighted pair group method with arithmetic mean analysis (UPGMA). The linkage disequilibrium coefficient (r2) was between 0.00 and 0.68, with an average of 0.04, which indicated a relatively low level of linkage disequilibrium among loci. The results of the association analysis revealed 55 significant marker-trait associations (MTA). Moreover, nine SSR markers were associated with multiple traits. This study provides tools with promising applications in the molecular selection and breeding of P. juncea germplasm.

Using Genomic Selection to Develop Performance-Based Restoration Plant Materials

Effective native plant materials are critical to restoring the structure and function of extensively modified ecosystems, such as the sagebrush steppe of North America’s Intermountain West. The reestablishment of native bunchgrasses, e.g., bluebunch wheatgrass (Pseudoroegneria spicata [Pursh] À. Löve), is the first step for recovery from invasive species and frequent wildfire and towards greater ecosystem resiliency. Effective native plant material exhibits functional traits that confer ecological fitness, phenotypic plasticity that enables adaptation to the local environment, and genetic variation that facilitates rapid evolution to local conditions, i.e., local adaptation. Here we illustrate a multi-disciplinary approach based on genomic selection to develop plant materials that address environmental issues that constrain local populations in altered ecosystems. Based on DNA sequence, genomic selection allows rapid screening of large numbers of seedlings, even for traits expressed only in more mature plants. Plants are genotyped and phenotyped in a training population to develop a genome model for the desired phenotype. Populations with modified phenotypes can be used to identify plant syndromes and test basic hypotheses regarding relationships of traits to adaptation and to one another. The effectiveness of genomic selection in crop and livestock breeding suggests this approach has tremendous potential for improving restoration outcomes for species such as bluebunch wheatgrass.

Incredible Role of Osmotic Adjustment in Grain Yield Sustainability under Water Scarcity Conditions in Wheat (Triticum aestivum L.)

Interrogations of local germplasm and landraces can offer a foundation and genetic basis for drought tolerance in wheat. Potential of drought tolerance in a panel of 30 wheat genotypes including varieties, local landraces, and wild crosses were explored under drought stress (DS) and well-watered (WW) conditions. Considerable variation for an osmotic adjustment (OA) and yield components, coupled with genotype and environment interaction was observed, which indicates the differential potential of wheat genotypes under both conditions. Reduction in yield per plant (YP), thousand kernel weight (TKW), and induction of OA was detected. Correlation analysis revealed a strong positive association of YP with directly contributing yield components under both environments, indicating the impotence of these traits as a selection-criteria for the screening of drought-tolerant genotypes for drylands worldwide. Subsequently, the association of OA with TKW which contributes directly to YP, indicates that wheat attains OA to extract more water from the soil under low water-potential. Genotypes including WC-4, WC-8 and LLR-29 showed more TKW under both conditions, among them; LLR-29 also has maximum OA and batter yield comparatively. Result provides insight into the role of OA in plant yield sustainability under DS. In this study, we figure out the concept of OA and its incredible role in sustainable plant yield in wheat.

Optimizing irrigation and nitrogen fertilization for seed yield in western wheatgrass [Pascopyrum smithii (Rydb.) Á. Löve] using a large multi-factorial field design

It is crucial for agricultural production to identify the trigger that switches plants from vegetative to reproductive growth. Agricultural sustainability in semiarid regions is challenged by nitrogen (N) fertilizer overuse, inadequate soil water, and heavy carbon emissions. Previous studies focused on the short-term effects of a single application of N and water but have not investigated the long-term effects of different irrigation and N fertilizer regimens on crop yields and yield components. N application is routinely coupled with water availability, and crop yields can be maximized by optimizing both. We examined the growth of western wheatgrass [Pascopyrum smithii (Rydb.) Á. Löve], a temperate-region forage and turf grass, using multiple different combinations of N fertilizer [(NH4)2·CO3] and irrigation levels over 3 years to determine optimal field management. We conducted multifactorial, orthogonally designed field experiments with large sample sizes, and measured fertile tillers m-2 (Y1), spikelets/fertile tillers (Y2), florets/spikelet (Y3), seed numbers/spikelet (Y4), seed weight (Y5), and seed yield (Z) to study factors associated with the switch between vegetative and reproductive growth. Fertilization had a greater effect on seed yield and yield components than irrigation. Y1 had the strongest positive effect on Z, whereas Y5 had a negative effect on Z. Irrigation and fertilization affected Z, Y1, and Y5. Fertilizer concentrations were positively correlated with Z, Y1, and Y5, whereas irrigation levels were negatively correlated. The ridge regression linear model results suggested N application rate and irrigation had antagonistic effects on Y1 (X3 = 867.6-4.23×X2; R2 = 0.988, F = Infinity, P<0.0001). We conclude that the optimal amount of N fertilizer and irrigation was 156 kg ha-1 + 115 mm for seed yield, 120 kg ha-1 + 146 mm for spikelets/fertile tillers, and 108 kg ha-1 + 119 mm for seed numbers/spikelets. These results will improve yield and reduce agricultural inputs for P. smithii in semiarid and arid regions, thereby reducing fertilizer pollution and conserving water.

Effects of ultrasonication on increased germination and improved seedling growth of aged grass seeds of tall fescue and Russian wildrye

The effects of ultrasonic treatments on the germination and seedling growth of aged tall fescue (Festuca arundinacea) and Russian wild rye (Psathyrostaehys juncea Nevski) seeds were determined using orthogonal matrix experimental design with four ultrasonic factors. The multivariate analysis of variance detected significant differences and coupling effects of the pair-wise factors. The activities of Superoxide Dismutase (SOD) and Peroxidase (POD) and the Malondialdehyde (MDA) content were affected. The ultrasonic treatments had positive effects on the germination percentage (GP) of the aged seeds and the growth of the seedlings (GS) and therefore we provided a basic evidence for the application of ultrasonic treatment to pretreat aged grass seeds. For the four ultrasonic factors, the optimal conditions were a sonication time of 36.7 min, a sonication temperature of 35 °C, an output power of 367 W and a seed soaking time 4.1 h after binary quadratic regressions analyses. The ultrasonic treatment has the potential to improve seedling growth. Moreover, the longevity of the tall fescue and the Russian wild rye seeds was approximately 9.5 and 11.5 years, respectively, under natural conditions of storage. The physiological mechanisms that might contribute to the improved GP and GS were discussed.

Toward a Reliable Evaluation of Forecasting Systems for Plant Diseases: A Case Study Using Fusarium Head Blight of Wheat

Despite great efforts to forecast plant diseases, many of the existing systems often fall short in providing farmers with accurate predictions. One of the main problems arises from the existence of year and location effects, so that more advanced procedures are required for evaluating existing systems in an unbiased manner. This paper illustrates the case of Fusarium head blight of winter wheat in Belgium. We present a new cross-validation strategy that enables the evaluation of the predictive performance of a forecasting system for years and locations that are different from the years and locations on which the forecast was developed. Four different cross-validation strategies and five regression techniques are used. The results demonstrated that traditional evaluation strategies are too optimistic in their predictions, whereas the cross-year cross-location validation strategy yielded more realistic outcomes. Using this procedure, the mean squared error increased and the coefficient of determination decreased in predicting disease severity and deoxynivalenol content, suggesting that existing evaluation strategies may generate a substantial optimistic bias. The strongest discrepancies between the cross-validation strategies were observed for multiple linear regression models.