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Bermudagrasses (Cynodon spp.) are the most preferred turfgrass species for athletic fields in the southern and transition zones of the United States. Developing and using bermudagrasses with superior traffic tolerance and surface playability under trafficked conditions benefits turfgrass managers, athletes, and sport organizations. A 2-year field study was conducted in Stillwater, OK, to quantify the genetic variability of traffic tolerance and surface playability from a population composed of two commercially available and 87 experimental interspecific hybrid bermudagrasses under fall simulated traffic stress. The experiment design was a randomized complete block design with three replications. Plots were subjected to 60 simulated cleat traffic events for 6 weeks in the fall of 2019 and 2020 using a Baldree traffic simulator. Bermudagrasses were evaluated for turfgrass quality (TQ), normalized difference vegetation index (NDVI), fall percent green cover (FPGC), shear strength (SS), and surface hardness (SH) after 3 and 6 weeks of traffic. Spring green-up percent green cover (SGPGC) was evaluated in the spring of 2020 and 2021. Except for SH, significant entry effects were found for all parameters and reliability estimates were moderate to high (i2 = 0.49 to 0.68) under simulated trafficked conditions. Experimental entries 17-4200-19X13, 17-4200-19X9, 17-4200-36X19, 17-5200-4X11, 18-7-2, 18-7-6, 18-8-2, 18-8-3, 18-8-7, 18-9-2, OSU1101, and OSU1664, and TifTuf® had excellent traffic tolerance. Entries 18-8-7, OSU1101, OSU1675, TifTuf®, and Tahoma 31® demonstrated high SS. There was a large group of entries that had consistent early spring green-up across both years, including Tilin#5, 18-9-8, OKC1221, OSU1257, OSU1318, OSU1337, OSU1406, OSU1439, OSU1651, OSU1675, Tahoma 31®, and TifTuf®. OSU1101 was the entry ranking in the top statistical grouping most often throughout the study. Findings illustrated the possibility of improving traffic tolerance and SS through breeding and using phenotypic selection could reliably select bermudagrass genotypes with improved traffic tolerance and SS in the transition zone.

There is a growing trend of cultivating hybrid bermudagrass [Cynodon dactylon (L.) Pers. × Cynodon transvaalensis Burtt-Davy] on golf course putting greens in the transition zone because of its excellent quality in the summer months, coupled with less pesticide input than creeping bentgrass (Agrostis stolonifera L.). However, the long-term success of bermudagrass putting greens is hindered by low temperatures in winter months, particularly in the transition zone. To address this issue, in addition to genetic improvement for cold hardiness through the development of new cultivars, effective management approaches are necessary to enhance the winter survival of putting green–type bermudagrass. The objective of this study was to investigate the relative freeze tolerance of four bermudagrasses and the effects of raising mowing height on the freeze tolerance of putting green–type bermudagrasses. In this study, two experimental putting green–type bermudagrasses (11X2 and OKC0805) along with cultivars TifEagle and OKC3920 were tested at two mowing heights (3.2 vs. 6.4 mm) at freeze temperatures that ranged between –4 and –11 °C. The lethal temperature to kill 50% of the population (LT50) as well as regrowth vigor during recovery were evaluated. Variety ‘OKC3920’ demonstrated enhanced freeze tolerance compared with ‘TifEagle’ at both mowing heights. Increasing the mowing height from 3.2 mm to 6.4 mm improved freeze tolerance for most genotypes tested in this study. After exposing the grasses to –8 °C for 1 hour, genotypes such as 11X2 exhibited better regrowth vigor and demonstrated a faster recovery. This study suggests that golf course managers can enhance winter resilience of bermudagrass putting greens by selecting genotypes strategically with superior freeze tolerance and raising mowing heights in the fall acclimation process.

Abstract Winterkill is a major concern for turf‐type bermudagrasses [Cynodon dactylon (L.) Pers. and C. dactylon (L.) Pers. × C. transvaalensis Burtt‐Davy] when cultivated in the US transition zone and frequently occurring drought conditions challenge bermudagrass management. Therefore, developing new bermudagrasses with both drought resistance and winter survivability are needed for the future sustainability of the turfgrass industry in the transition zone. How these two traits interact within bermudagrass populations is not widely reported. Accordingly, the objectives of this study were to estimate reliability for spring green‐up (SG) (as an indicator of winter survivability) and drought response in turf‐type bermudagrasses. A total of 77 experimental genotypes and 7 cultivars were planted in a randomized complete block design with 3 replications over 2 years in Goodwell, OK, USA. Substantial genetic variations were found for drought response and SG parameters when analyzed by year, but variations for SG parameters and leaf firing were not significant when analyzed across years. The reliability estimates for drought response ranged from low to moderate (i2 = 0.24–0.61), whereas the subsequent estimates for SG were low (i2 = 0.13–0.33), suggesting poor reliability. The low‐to‐moderate reliability values in SG were likely due to antecedent drought injuries, indicating that winter survivability and drought resistance may need to be evaluated in separate trials.

Abstract It is more and more important to analyse and process complex data for gaining more valuable knowledge and making more accurate decisions. The multigranulation decision theory based on conditional probability and cost loss has the advantage of processing decision‐making problems from multi‐levels and multi‐angles, and the neighbourhood rough set model (NRS) can facilitate the analysis and processing of numerical or mixed type data, and can address the limitation of multigranulation decision‐theoretic rough sets (MG‐DTRS), which is not easy to cope with complex data. Based on the in‐depth study of hybrid‐valued decision systems and MG‐DTRS models, this study analysed neighbourhood MG‐DTRS (NMG‐DTRS) deeply by fusing MG‐DTRS and NRS; a matrix‐based approach for approximation sets of NMG‐DTRS model was proposed on the basis of the matrix representations of concepts; the positive, boundary and negative domains were constructed from the matrix perspective, and the concept of positive decision recognition rate was introduced. Furthermore, the authors explored the related properties of NMG‐DTRS model, and designed and described the corresponding solving algorithms in detail. Finally, some experimental results that were employed not only verified the effectiveness and feasibility of the proposed algorithm, but also showed the relationship between the decision recognition rate and the granularity and threshold.

Bermudagrass (Cynodon sp.) is one of the most commonly used warm-season turfgrasses in the southern areas and transition zone of the United States. Due to the increasing demand for water resources and periodic drought, it is important to improve the drought resistance of bermudagrass for water savings and persistence under drought stress. This study was conducted to determine whether experimental bermudagrass genotypes have improved drought resistance compared with the standard cultivars Tifway and Riley’s Super Sport (Celebration®) at Stillwater, OK. The trials were designed as randomized complete blocks with four replications in Expt. I and three replications in Expt. II. In each experiment, genotypes were subjected to progressive acute drought conditions using polyethylene waterproof tarps to exclude precipitation over a period of at least 72 d. Bermudagrass entries were evaluated for turfgrass quality, leaf firing, normalized difference vegetation index, and live green cover at least once each week during the dry-down. Substantial drought response variations were found in this study, and all parameters were positively and highly correlated. A turf performance index (TPI) was assembled based on the number of times an entry ranked in the top statistical group across all testing parameters on each date. ‘DT-1’ (TifTuf®) and OSU1221 had the top TPI in both experiments. Most of bermudagrass experimental genotypes had equal or greater TPI than the standard Tifway, showing improved drought resistance through breeding effects. The identification of superior drought resistance experimental genotypes provided useful information to breeders on cultivar release.

In the Southern Great Plains, wheat cultivars have been selected for a combination of outstanding yield and drought tolerance as a long-term breeding goal. To understand the underlying genetic mechanisms, this study aimed to dissect the quantitative trait loci (QTL) associated with yield components and kernel traits in two wheat cultivars `TAM 112' and `Duster' under both irrigated and dryland environments. A set of 182 recombined inbred lines (RIL) derived from the cross of TAM 112/Duster were planted in 13 diverse environments for evaluation of 18 yield and kernel related traits. High-density genetic linkage map was constructed using 5,081 single nucleotide polymorphisms (SNPs) from genotyping-by-sequencing (GBS). QTL mapping analysis detected 134 QTL regions on all 21 wheat chromosomes, including 30 pleiotropic QTL regions and 21 consistent QTL regions, with 10 QTL regions in common. Three major pleiotropic QTL on the short arms of chromosomes 2B (57.5 - 61.6 Mbps), 2D (37.1 - 38.7 Mbps), and 7D (66.0 - 69.2 Mbps) colocalized with genes Ppd-B1, Ppd-D1, and FT-D1, respectively. And four consistent QTL associated with kernel length (KLEN), thousand kernel weight (TKW), plot grain yield (YLD), and kernel spike-1 (KPS) (Qklen.tamu.1A.325, Qtkw.tamu.2B.137, Qyld.tamu.2D.3, and Qkps.tamu.6A.113) explained more than 5% of the phenotypic variation. QTL Qklen.tamu.1A.325 is a novel QTL with consistent effects under all tested environments. Marker haplotype analysis indicated the QTL combinations significantly increased yield and kernel traits. QTL and the linked markers identified in this study will facilitate future marker-assisted selection (MAS) for pyramiding the favorable alleles and QTL map-based cloning.

Understanding genetic architectures of yield and yield-related traits of wheat (Triticum aestivum L.) grown under dryland or irrigated conditions is pivotal for developing modern high-yielding germplasm and cultivars. The objectives of this study were to detect quantitative trait loci (QTL) linked to yield and yield components using a mapping population derived from ‘TAM 113’/‘Gallagher’, including 191 recombinant inbred lines (RILs). The population was grown in McGregor, College Station, and Bushland, Texas, for three consecutive years from 2019 to 2021. A high-density genetic map covering all 21 chromosomes was constructed using a set of 8,075 single nucleotide polymorphisms (SNPs) obtained using genotyping-by-sequencing (GBS). A total of 147 QTLs for 16 yield-related traits were identified, which included 16 QTLs consistently detected in multiple experiments and 8 QTLs that showed pleiotropic effects. Of them, five pleiotropic QTLs overlapped with the consistent QTL. They increased grain yield (YLD) up to 37.64 g m−2, thousand kernel weight (TKW) up to 1.33 g, harvest (HI) up to 0.97%, kernel length up to 0.08 mm, and kernel width up to 0.04 mm with Gallagher alleles and increased YLD up to 22.21 g m−2, kernels spike−1 up to 1.77, TKW up to 1.14 g, and HI up to 3.72% with TAM 113 alleles. One major and consistent QTL on chromosome 2D at 34.4 Mbp overlapped with the major photoperiod gene Ppd-D1 and was affected by multiple traits, including kernel diameter (DIAM), TKW, kernel hardness index (KHI), heading date (HD), and plant height (PH). Another QTL cluster region on 7D between 52 and 66 Mbp, encompassing one consistent and three pleiotropic QTLs. One of the pleiotropic QTLs at 52 Mbp increased YLD up to 24.16 g m−2, HI up to 1%, and DIAM up to 0.03 mm. This study dissected genetic loci associated with yield and yield-related traits, providing valuable information on wheat improvement using marker-assisted selection (MAS).

Suitable tensile strength is essential for sod harvest, transport, and installation. Thirty-nine bermudagrass (Cynodon sp.) entries were evaluated for sod handling quality (SHQ) and sod tensile strength (STS) during 2014–15. The SHQ (a discontinuous qualitative parameter) was evaluated using a 1 to 5 scale with 1 = complete pad separation during handling and 5 = no cracking or separation in the sod pad with excellent quality. The STS (a quantitative parameter) was determined using the force required to shear/separate the sod pad. Sod harvests were conducted at 14, 22, and 24 months after planting (MAP). The entry, harvest date, and their interaction affected STS and SHQ. Entries OKC 1302 and 12-TSB-1 had greater STS than ‘Patriot’ but less STS than ‘Latitude 36’, ‘Tifway’, ‘Astro’, and ‘TifGrand’. The seeded entry PST-R6T9S had the lowest STS and SHQ. The overall mean STS and SHQ were lowest at 22 MAP, which could be attributed to the slow recovery of the entries after Winter 2014. A strong positive correlation (r = 0.92) between STS and SHQ suggests that SHQ can be used as a rapid field method to estimate suitability for sod harvest. A predictive linear relationship between overall STS and overall SHQ (r2 = 0.85) found predicted STS values of 8.5, 22.6, 36.8, and 51.0 kg⋅dm–2 for overall mean SHQ ratings of 2, 3, 4, and 5, respectively. The results of this work will help sod producers in cultivar selection and will aid breeders in making commercialization decisions.

Cannabis (Cannabis sativa L.) is a dioecious plant cultivated worldwide for thousands of years. Besides the narcotic and therapeutic effects, Cannabis can be used as raw materials in multiple fields, including bioenergy, textiles, food, and ecological restoration. It is also an efficient bioremediation agent for contaminated soil, as well as greenhouse gas absorption. With the expansion of the market, there has been an increased demand to develop Cannabis cultivars with enhanced traits. As a major science breakthrough, the advent of the CRISPR/Cas system will revolutionize the basic and applied research in Cannabis. This article provides an overview of the recent advances in the optimization of a transformation system and in the gene editing of Cannabis. To achieve the full potential of this environmentally friendly and sustainable crop, we highlight future directions of genetic modification as well as several bottlenecks to overcome.

Abstract Cynodon transvaalensis Burtt‐Davy is frequently used to cross with C. dactylon Pers. in the creation of F1 hybrid cultivars that are some of the most widely used in the worldwide turf industry. However, molecular resource development in this species is limited. Accordingly, the objectives of this study were to construct a high‐density genetic map, and to identify genomic regions associated with establishment rate. In this study, we constructed the first high‐density linkage map for African bermudagrass using a genotyping by sequencing approach based on 109 S1 progenies. A total of 1,246 single nucleotide polymorphisms and 32 simple sequence repeat markers were integrated in the linkage map. The total length of nine linkage groups was 882.3 cM, with an average distance of 0.69 cM per interval. Four genomic regions were identified to be associated with sod establishment rate. The results provide important genetic resources towards understanding the genome as well as marker‐assisted selection for improving the establishment rate in bermudagrass breeding.

To meet the demands of different wheat-based food products, traits related to end-use quality become indispensable components in wheat improvement. Thus, markers associated with these traits are valuable for the timely evaluation of protein content, kernel physical characteristics, and rheological properties. Hereunder, we report the mapping results of quantitative trait loci (QTLs) linked to end-use quality traits. We used a dense genetic map with 5199 SNPs from a 90K array based on a recombinant inbred line (RIL) population derived from ‘CO960293-2’/‘TAM 111’. The population was evaluated for flour protein concentration, kernel characteristics, dough rheological properties, and grain mineral concentrations. An inclusive composite interval mapping model for individual and across-environment QTL analyses revealed 22 consistent QTLs identified in two or more environments. Chromosomes 1A, 1B, and 1D had clustered QTLs associated with rheological parameters. Glu-D1 loci from CO960293-2 and either low-molecular-weight glutenin subunits or gliadin loci on 1A, 1B, and 1D influenced dough mixing properties substantially, with up to 34.2% of the total phenotypic variation explained (PVE). A total of five QTLs associated with grain Cd, Co, and Mo concentrations were identified on 3B, 5A, and 7B, explaining up to 11.6% of PVE. The results provide important genetic resources towards understanding the genetic bases of end-use quality traits. Information about the novel and consistent QTLs provided solid foundations for further characterization and marker designing to assist selections for end-use quality improvements.

It is an important subject to mine valuable knowledge from complex and massive data in the era of big data. Rough set theory is a new mathematical tool for dealing with uncertain and inaccurate data, decision-theoretic rough set model (DTRS), as an extension of classical rough set model, is used to analyze decision information systems and multi-granulation decision-theoretic rough set model (MG-DTRS) can analyze and process target concepts from different angles and levels. However, the classical DTRS model exists some limitations in dealing numerical or hybrid-valued data. Considering the different influence of numerical features and symbolic features on decision-making, the paper proposes an incomplete neighborhood multi-granulation decision-theoretic rough set model in hybrid-valued decision system through integrating MG-DTRS with neighbourhood rough sets, and two types of neighborhood multi-granulation decision-theoretic set models are emphatically analysed. Furthermore, taking pessimistic and optimistic neighborhood multi-granulation decision-theoretic rough sets as examples, the implementation algorithms and related properties of the two type of models are studied. Finally, the relationship between the proposed model and other models is analyzed through formula derivation. The model proposed in this paper can effectively solve the decision-making problem of hybrid-valued incomplete information system through multi-angle and multi-level analysis.

Soil datasets, including soil sample data and soil map products, often contain outliers that can lead to inaccurate modeling and analysis of various soil-related issues. Existing methods for identifying potential outliers in soil datasets rely on simple statistical approaches and tend to overlook the geographical characteristics of the soil. Local indicators of spatial association (LISA) can address this limitation by examining the local spatial structures inherent in soil data. However, distinguishing some outliers remains challenging because of the varying levels of heterogeneity across different soil regions. In this paper, we present a novel method for recognizing potential outliers through local heterogeneity enhancement, which is aimed at improving the quality of soil datasets. In this method, stratified soil variations are first balanced to mitigate the effects of spatial discrepancies in different soil regions. Second, local heterogeneity enhancement is conducted to modify the outlier scores associated with abnormal soils exhibiting low heterogeneity. Third, a frequency histogram of outlier scores is applied to determine a suitable threshold at which to recognize potential abnormal values in soil datasets. To validate the proposed method, it was compared with the LISA and box-plot methods. Simulation data and soil data were adopted in the experiment, incorporating two types of irregular points and spatially continuous surfaces. The comparative experiments demonstrated that the proposed method more effectively identifies potential outliers by analyzing and balancing the local spatial structure of the soil than traditional methods do. It can be concluded that local heterogeneity enhancement is beneficial for recognizing potential outliers in soil datasets.