Crop Science最新文献

Beard and Green compiled one of the earliest reviews on the environmental and societal (cultural) benefits that living turfgrass systems (e.g., home lawns, athletic fields, golf courses, roadsides, and grounds) provide to humans and associated contemporary issues with turfgrass. Today, the benefits of vegetation systems are called ecosystem services, and the associated negative aspects are called disservices. Since 1994, a significant amount of research has been conducted to further understand these ecosystem services and disservices and discover new ecosystem services and disservices, which we summarize and identify the knowledge gaps in this review. Turfgrass systems provide positive economic benefits to the US economy and help increase property values; however, many of these ecosystem services are environmental and societal. Some environmental services include (1) improving soil health, quality, and stability; (2) oxygen production; (3) reducing stormwater runoff; (4) filtering water to protect waterways and recharging groundwater; (5) providing evaporative cooling and reducing sunlight glare to improve human comfort levels; (6) offering vertebrate and invertebrate habitat; and (7) offering solutions for recycling wastewater and biosolids. Some societal (cultural) services include (1) outdoor spaces that improve human mental and physical health, (2) increasing community and social harmony, (3) helping deter crime, and (4) reducing human contact with noxious weeds and human-disease insect vectors. Research, cooperative extension, and education efforts must be increased on these topics to continue to provide additional evidence of these ecosystem services to the public, policymakers, turfgrass practitioners, homeowners, students, and future generations.

Climate change, recurrent droughts, and increasing urban water demands have limited water availability in urban landscapes. Water quantity challenges have led to irrigation restrictions and turfgrass removal programs. An experiment was conducted at the University of Florida, West Florida Research and Education Center, Jay, FL, to evaluate the effect of turfgrass conversion to other landscape types on nutrient leaching and groundwater recharge. In April 2021, all surface vegetation was removed from existing turfgrass plots using a sod harvester. Thereafter, plots were planted or covered with three landscape types: a pollinator landscape with flowering forbs (Mimosa sp., Coreopsis sp., and Phyla sp.) + turfgrass (Eremochloa ophiuroides); a nitrogen (N)-efficient lawn (Arachis glabrata + Paspalum notatum); and a low-input landscape with unplanted woodchip mulch. Undisturbed turfgrass (E. ophiuroides) served as a control. For 2 years, leachate samples were collected weekly from previously installed 168-L drainage lysimeters for NO₃-N and NH₄-N load determination. Temporal changes in landscape composition, groundwater recharge, water use, and soil bulk density were also quantified. While the mulch leached 44.7 kg ha⁻¹ NO₃-N year⁻¹, this landscape still offers positive attributes, including erosion protection and water conservation. Conversely, the pollinator landscape minimized nitrogen leaching (8.3 kg ha⁻¹ NO₃-N year⁻¹) due to their relatively greater water use rates (3.56 mm day⁻¹). The turfgrass and nitrogen-efficient lawn returned ∼35% of the water inputs as groundwater recharge while maintaining relatively low nitrogen leaching (3.6 and 2.7 kg ha⁻¹ NO₃-N year⁻¹, respectively), making these landscapes efficient for protecting both water quality and quantity.

Brassica carinata A. Braun (carinata) has become an important oil crop for biofuel production in subtropical regions. Carinata is highly sensitive to acetolactate synthase (ALS)-inhibiting herbicides, limiting its introduction into existing crop rotations. The objective of the study was to develop carinata lines resistant to ALS-inhibiting herbicides. A susceptible carinata line was crossed with a resistant Brassica napus L. line. Lines derived from those crosses were screened at high doses of imidazolinones, which allowed identifying five lines with high levels of resistance. Doses to reduce plant growth 50% (GR₅₀) and cause 50% injury (ID₅₀) were four to nine times greater than susceptible lines. Resistant lines exhibited cross resistance with halosulfuron (sulfonylurea). Resistance was confirmed under field conditions with doses 2X and 4X for imazethapyr and 4X–8X for halosulfuron of their respective label doses. While susceptible lines died, resistant lines exhibited no injury or growth reductions compared with nontreated controls. Sequencing of the ALS gene indicated that all resistant lines carried a Trp574Leu amino acid substitution, a mutation responsible for resistance in other species. Crosses between resistant lines and a susceptible line demonstrated that the inheritance of the mutation corresponded with the resistance phenotype in the F2. The resistance trait behaved as a single, fully dominant allele, which makes it easier to transfer it to carinata lines with desirable agronomic traits. The resistant lines developed here provide flexibility for use in multiple crop rotations and opens the possibility to use ALS-inhibiting herbicides for weed control within this crop's growing season.

Brown rust (caused by Puccinia melanocephala H. & P. Sydow) is one of the most devastating diseases in commercial sugarcane production. It could reduce sugarcane yield by up to 50% depending on the susceptibility levels of cultivars. Breeding disease-resistant cultivars is the most effective, economical, and environmentally friendly option to control brown rust. A genome-wide association study was conducted on a field trial using 432 sugarcane clones following an augmented design with two replications. Brown rust was screened using the whorl inoculation method over two crop cycles. The genotype data were obtained through target enrichment sequencing technologies. The gene actions considering six different models and marker dosage effects were included during the marker-trait analysis. A total of seven, nine, and seven nonredundant marker-trait associations were identified for plant cane, first ratoon, and across two crop cycles, respectively. The most significant (p-value 6.17E⁻²⁰) marker (chr01p59833543) has the additive effect of −0.63 for the diplo-additive model and reduced disease severity the most (41.35%) due to heterozygote (AG) over homozygote allele (AA) combination in the tested clones. Gene annotation of the monoploid sugarcane genome R570 suggested that six putative candidate genes were co-located with significant markers associated with brown rust resistance in sugarcane. The putative candidate genes regulated the formation of a cell wall barrier that plays a crucial role in controlling brown rust pathogen infection. The results of this study will open the path to exploiting new resistance sources for brown rust resistance in commercial sugarcane.

Breeders made remarkable progress in improving productivity and stability of cultivars. Breeding progress relies on selecting favorable alleles for performance and stability to produce productive varieties across diverse environments. In this study, we analyzed the Genomes to Fields Initiative 2018–2019 genotype by environment interaction (G × E) dataset, focusing on three populations of double haploid (DH) lines derived from crossing inbrexpired Plant Variety Protection (ex-PVP) inbred line PHW65 with inbred lines PHN11, Mo44, and MoG. PHW65 is an Iodent/Lancaster-type inbred; PHN11 is an Iodent type ex-PVP line; Mo44 is a tropical-derived inbred; and MoG is an agronomically poor line derived from the variety Mastadon. Hybrids were produced by crossing the resulting DHs with Stiff Stalk testers PHT69 and LH195. The study's objective was to determine the donor inbreds' relative value and understand the impact of selection history on genomic prediction. We conducted a two-stage analysis to compare hybrid performance and G × E variance of the populations. G × E variance for yield was significantly lower in the PHW65 × PHN11 population relative to the PHW65 × MoG population. The reduced G × E variance of the PHN11 population led to increased indirect prediction accuracy (when training and testing data are drawn from the same population but different environments). In cross-validation, the PHN11 population had the greatest indirect prediction accuracy 45% of the time, followed by the Mo44 population (30%) and the MoG population (25%). Results demonstrate that prediction accuracy was greater in the population with the longest history of selection for favorable alleles (PHN11), contributing to greater yield stability.

Vegetation indices have become an indispensable tool in remote sensing-based agricultural research. A recent area of advancement in agricultural remote sensing research is in high-throughput phenotyping, often conducted on a plot by plot basis. FIELDimageR is a tool used extensively in high-throughput phenotyping that estimates zonal statistics of vegetation indices per plot. However, being written in R language, FIELDimageR requires high computing time. As a high-resolution image over a large area means a large number of pixels, FIELDimageR is incapable of using high-resolution orthomosaicked images without reducing image resolution by aggregating digital numbers of several pixels and treating them as one pixel. This research tool implements FIELDimageR in the Python language as FIELDimagePy. FIELDimagePy follows similar workflows as FIELDimageR and generates equivalent results for zonal statistics of vegetation indices per plot. FIELDimagePy is significantly and substantially faster than FIELDimageR. Computing time by FIELDimagePy are three to four times lower than computing times by FIELDimageR, even when using raw images with 16 times denser pixels. Moreover, FIELDimagePy is useful beyond plot by plot research in agriculture and capable of estimating zonal statistics of any raster bounded by any polygons. With slight modifications, FIELDimagePy can be useful for other disciplines of science, such as geophysics, geography, economics, medical sciences, among others. FIELDimagePy can be accessed from the GitHub repository: https://github.com/SumantraChatterjee/FIELDimagePy.

Field surveys were conducted on golf courses reporting the inability of imidacloprid to control white grubs (Coleoptera: Scarabaeidae) when applied preventively. Surveys of five sites with significant past imidacloprid use (>10 years) revealed significantly greater white grub populations in rough-mown turf following imidacloprid treatment than that of adjacent short-mown fairways. Additionally, spatial analysis by distance indicEs (SADIE) analyses demonstrated a positive correlation between white grub and thatch spatial patterns. To investigate the impact of thatch on imidacloprid efficacy and translocation throughout the turfgrass plant, greenhouse experiments were conducted using turf with differing thatch levels. Imidacloprid concentrations in soil and plant tissues were measured with high-performance liquid chromatography (HPLC) and compared to values obtained through an enzyme-linked immunosorbent assay (ELISA) kit to determine if the latter could be a cost-effective alternative in future studies. ELISA provided reliable estimates of concentrations of imidacloprid compared to HPLC, with only minor discrepancies noted across different types of treatments and assessment timings. Despite finding higher imidacloprid levels in leaf tissues compared to roots and some differences in concentration across thatch treatments, there was no clear pattern showing that thatch thickness significantly affects imidacloprid penetration or accumulation in plant tissues or soil over time. These findings suggest that factors other than thatch thickness may contribute to the observed field failures of imidacloprid in controlling white grubs. Further research is necessary to identify these factors and optimize the use of imidacloprid in turfgrass pest management strategies.

In genomic selection (GS), the prediction accuracy is heavily influenced by the composition of the training set (TS). Currently, two primary strategies for building TS are used: one involves accumulating historical phenotypic records from multiple years, while the other is the “test-and-shelf” approach. Additionally, studies have suggested that optimizing TS composition using genetic algorithms can improve the accuracy of prediction models. Most breeders operate in open systems, introducing new genetic variability into their populations as needed. However, the impact of elite germplasm introduction in GS models remains unclear. Therefore, we conducted a case study in self-pollinated crops using stochastic simulations to understand the effects of elite germplasm introduction, TS composition, and its optimization in long-term breeding programs. Overall, introducing external elite germplasm reduces the prediction accuracy. In this context, test and shelf seem more stable regarding accuracy in dealing with introductions despite the origin and rate, being useful in programs where the introductions come from different sources over the years. Conversely, using historical data, if the introductions come from the same source over the cycles, this negative effect is reduced as long as the cycles and this approach become the best. Thus, it may support public breeding programs in establishing networks of collaborations where the exchange of germplasm will occur at a predefined rate and flow. In either case, the use of algorithms of optimization to trim the genetic variability does not bring a substantial advantage in the medium to long term.

Turfgrass seeds are often sold as mixtures of several species to increase the probability of positive responses toward abiotic and biotic stresses, a response to drought being one of these. Several species of turfgrass are already thought to be better suited for drought, such as hard fescue (Festuca brevipila Tracey) and tall fescue [Schedonorus arundinaceus (Schreb.) Dumort]. However, little is known about the benefit of these species in mixtures with drought-intolerant and/or drought-avoiding species during drought. Understanding species mixture composition during establishment, before and after drought stress periods, could help develop more resilient mixtures for this stress condition. We compared monocultures and mixtures of hard fescue, Kentucky bluegrass (Poa pratensis L.), and perennial ryegrass (Lolium perenne L.) during sequential short drought and recovery periods in controlled conditions. We observed that the composition of most mixtures remained similar during drought and recovery periods; however, perennial ryegrass was often less prevalent after drought stress. We found that hard fescue monocultures had better green leaf coverage than Kentucky bluegrass and perennial ryegrass during drought stress. However, the presence of hard fescue in mixtures was not an indicator of greater drought tolerance, and variable fluorescence to maximal fluorescence data indicated that hard fescue was just as physiologically stressed as perennial ryegrass and Kentucky bluegrass during the drought periods. These results indicate that while hard fescue seems visually drought tolerant, it is still physiologically stressed and improved drought tolerance could be achieved through focusing on physiological indicators of stress in this species rather than visual indicators.

Spring wheat (Triticum aestivum L.) is a popular bread wheat with high milling and baking requirements. Vernalization is not required for spring wheat, which allows for fast growth under manipulated conditions. This experiment's objective was rapid development of inbred lines of hard red spring wheat throughout the off-season and preserve enough genetic variability to perform selection. A total of 1575 F₂ heads from three distinct populations were randomly harvested in the field-season 2021. To break seed dormancy, seeds were held for 2 days at 0°C. Three breeding cycles were performed through single seed descent under manipulated growth condition during the off-season 2021–2022. We were able to harvest plant materials as quickly as 54 days after planting in comparison to 110 days average field season. We lost a total of 36.4% during the three off-season fast advancement generations and 711 genotypes reached the F_5:6 generation with enough seed to perform a partially replicated small plot yield trial at two locations during the 2022 field-season. Response traits collected included grain yield, grain protein, plant height, days to heading, and bacterial leaf streak (Xanthomonas transluens) disease severity. Heritability of collected traits varied between 0.61 and 0.92. Although we had considerable loss during the speed breeding, we were able to identify superior genotypes compared to the parents.