Crop Science最新文献

Using soil cover residues from previous crops through integrated systems has proven effective in driving changes in soil properties with nutrient cycling, promoting higher grain production. However, there is still a need to investigate the changes that different cultivation arrangements of these management systems can influence on soybean productivity. The aim was to compare conventional soybean cultivation methods with integrated systems in a tropical region over 2 years and how these systems affect desiccation efficiency, biomass decomposition, carbon/nitrogen ratio, nutrient cycling, as well as soybean productivity. An experimental area, under a block design with three replications, with conventional soybean cultivation system with soybean cultivated over crop residues produced by a previous integration of maize, three cultivars of Panicum maximum (Tamani, Quenia, and Zuri guinea grasses), and pigeon pea, arranged in monoculture and triple intercropping, it was implemented in Latossolo Vermelho Acriférrico typical, Goiás, Brazil. The results indicated that Tamani and Quenia guinea grasses, along with pigeon pea, exhibited higher desiccation efficiency in both monoculture and intercropping. The previous integration of maize with Panicum cultivars and pigeon pea increased soil coverage and maximized nutrient cycling, resulting in increasing productivity gains by approximately 39.8% compared to soybean cultivation without biomass covering the soil. These results highlight the importance of considering nutrient cycling and decomposition rates in fertilization strategies to increase the sustainability of systems. Therefore, integrated systems, which combine grasses and legumes, represent a promising and efficient strategy for agricultural production systems.

Prediction-based breeding reshapes plant genetic improvement by prioritizing the predictive ability of models over causal interpretation. This review examines recent advances in the use of tools such as genomic selection, high-throughput phenotyping, multi-omics integration, and enviromics to enhance genetic gain and improve the efficiency of breeding programs. Predictive models, while powerful, rely on validation within the genetic and environmental domains represented in the training set, with evident risks when extrapolated to unrelated scenarios. Traditional approaches such as marker-assisted selection and genome-wide association study remain limited for quantitative traits, reinforcing the need for prediction-oriented models. Moreover, the expansion of omics data sources, although capturing greater biological complexity, must be accompanied by rigorous validation practices to avoid fragile interpretations. Stochastic simulations are a strategic tool for testing selection schemes, optimizing training populations, anticipating overfitting risks, reducing costs, and guiding decisions based on prospective scenarios. This review also highlights the importance of ensuring independence between calibration and prediction, focusing on practical accuracy evaluation, and prioritizing operational utility over mechanistic explanation. In summary, prediction-based breeding is a core strategy for modernizing breeding programs, connecting computational tools, high-dimensional data, and pragmatic decision-making to deliver consistent results.

The aim of this research was to assess the natural resistance of traditional maize (Zea mays L.) varieties cultivated in Espírito Santo, Brazil, to Spodoptera frugiperda. Additionally, the study aimed to investigate the leaf structures, chemicals, biochemicals, and cellular factors associated with this resistance, with the ultimate goal of identifying promising varieties for genetic breeding. The evaluation included a total of 77 traditional maize varieties, along with four commercial varieties cultivated throughout Brazil. The experiment followed a randomized complete block design with three replications, and the plots consisted of three rows, each 3 m in length and spaced 1.0 m apart, with the central row representing the useful area of the plot. Traditional varieties exhibiting low herbivory by S. frugiperda underwent antixenosis tests for feeding and oviposition. Leaf samples were subjected to histology and microscopy tests. Out of the field tests, eight maize varieties displayed no evidence of S. frugiperda attacks, suggesting either natural resistance or antixenosis by the pest. Antixenosis analysis identified variety Cativerde 02 with minimal herbivory by S. frugiperda, comparable to the negative control, the transgenic hybrid Feroz Viptera 3 (Syngenta), known for its resistance to the pest. Histological and microscopy analyses revealed distinct chemical elements in the traditional Cativerde 02 compared to the controls, and the presence of silica crystals in this variety suggested a potential protective mechanism against herbivory.

Stem rust, caused by Puccinia graminis Pers. f. sp. tritici Eriks. & E. Henn (Pgt), is one of the most widely spread fungal diseases in wheat (Triticum aestivum) worldwide. Sr43 originated from Thinopyrum ponticum and showed broad-spectrum resistance to stem rust. Recently, Sr43 has been cloned, which encodes an unusual protein kinase fused to two domains of unknown function. However, high-throughput Sr43 diagnostic markers are not available to assist wheat breeding. In this study, we identified sequence variation within or near the conserved domains of Sr43 by comparing the Sr43 sequence to its homologs in the wheat pangenome accessions and developed two kompetitive allele-specific PCR (KASP) markers (Sr43-KASP1 and Sr43-KASP2) for Sr43. The Sr43-marker alleles at both marker loci co-segregated with the corresponding phenotypes in a BC₂F_2:3 segregating population developed by backcrossing RWG34 (Sr43⁺) to a hard winter wheat (HWW) “Jagger” (Sr43⁻). The Sr43 resistance marker alleles were not detected in a US HWW panel (RGON2020) where Sr43 is absent. The results confirmed that Sr43-KASP1 and Sr43-KASP2 are diagnostic for Sr43 and will facilitate the effective deployment of Sr43 to improve stem rust resistance in wheat breeding programs.

Nixtamalization moisture content, a measure of the quantity of water absorbed during the nixtamalization of a grain, has a large impact on the end-quality of masa-based products. An application to predict nixtamalization moisture content from raw inbred and hybrid maize (Zea mays L.) grain was recently developed, but its utility in a breeding context has not been assessed. Here, important breeding considerations such as partitioning of variation, genetic architecture, and relationship with yield were assessed in diverse maize hybrids (n = 560), modern commercial hybrids (n = 10), and historically high-acreage hybrids (n = 15) grown in up to three environments across 2 years. This study demonstrated that nixtamalization moisture content is heavily influenced by growing conditions, but sufficient genetic variance is present to allow breeders to make gains from selection. There was not a substantial correlation between nixtamalization moisture content and yield, suggesting breeders can select for both traits without negatively impacting either trait. Both additive and dominant genetic action was observed, and genomic prediction was able to predict nixtamalization moisture content in hybrids with an average Spearman's rank correlation coefficient between 0.253 and 0.451 and a root mean square error between 0.00579 and 0.00691. The findings suggest that nixtamalization moisture content can be selected for early in breeding generations, allowing breeders to develop improved food-grade maize germplasm without negatively impacting yield.

Kernza intermediate wheatgrass (IWG) [Thinopyrum intermedium (Host) Barkworth & D.R. Dewey] is a promising perennial grain and forage crop, but experiences grain yield decline, potentially due to limited nitrogen (N) and stand overcrowding. We evaluated the effects of N fertilization and stand thinning on grain and forage yield, weed biomass, thousand-kernel weight (TKW), and harvest index (HI). We used a full factorial design with N rates of 0, 75, and 150 kg N ha⁻¹ and thinning intensities of 0%, 25%, 38%, or 50% stand density reduction via banded herbicide at two locations in Wisconsin over 2 years. Fertilization and thinning did not interact. Grain yields increased with N fertilization except at Madison in Year 2. At Lancaster, grain yield increased from 293 with no N to 497 and 701 kg ha⁻¹ with 75 and 150 kg N ha⁻¹, respectively, across years. At Madison, grain yield increased only in Year 1. Forage mass also increased with N at both sites except Madison in Year 2. At Lancaster, forage mass ranged from 4016 to 6500 kg ha⁻¹ across years and N rates. TKW and HI increased with N at both sites, except at Madison in Year 2. Weed biomass was unaffected by treatments. Thinning had no effect on grain yield at Lancaster in Year 1, but in Year 2, grain yield increased from 368 to 505 kg ha⁻¹ with 50% thinning. These results suggest that applying 75 kg N ha⁻¹ is important for maintaining IWG productivity and that thinning can help sustain grain yield in older stands.

The drive to increase seed yield in soybean [Glycine max (L.) Merr.] has traditionally overshadowed the exploration of biomass partitioning and the compositional characteristics of plant residue traits such as leaves, petioles, stems, and pods. The exploration of biomass partitioning and the compositional characteristics of plant residue traits in soybean provide insights into plant nutrient allocation strategies that can be utilized to increase crop productivity and improve management practices for maximizing yields and sustainability. Recognizing this gap, our study aimed to investigate the variability in these traits across 32 genetically diverse soybean genotypes cultivated over 2 years in central Iowa. Through detailed collection and analysis of vegetative parts at critical growth stages (R1, R4, and R8), we assessed both biomass traits and their chemical compositional characteristics, focusing on soybean residue traits to enhance soil health and their importance in soybean cropping systems. We present broad sense heritability estimates for accumulated (R8) organ biomass (0.61–0.87) and residue carbon nitrogen composition (0.74) in soybeans. The large variation and high heritability suggest breeding strategies to optimize variety development via biomass and residue traits. Utilizing the Agriculture Production Systems sIMulator, we conducted a sensitivity analysis to evaluate the impact of soybean residue quality on soil nutrient cycling and its effects on the subsequent maize [Zea mays L.] crop. The study underscores the importance of soybean residue management, emphasizing the need for integrated approaches in breeding and agricultural practices that utilize the genetic diversity of these traits.

Recent climate variability and emerging diseases have led farmers toward using multiple tactics to protect corn (Zea mays L.) yield, including fungicide applications. Despite observed yield increases from various fungicide application methods in corn, minimal research has addressed the yield component mechanisms driving these gains. Therefore, this research examined how fungicide application methods can impact corn grain fill duration, kernel weight, and grain yield. In 2022 and 2023, a research trial was established in West Lafayette, IN to examine kernel weight accumulation differences between applied fungicide treatments. In addition, two research trials were sampled in Indiana to assess corn harvest kernel number, kernel weight, and yield differences between designated treatments. Examined treatments include the following: (1) nontreated control treatment, (2) subsurface banded fungicide (flutriafol) applied at planting (starter), and (3) foliar fungicide (prothioconazole, trifloxystrobin, and fluopyram) applied at the R1 growth stage. Trial results observed corn grain yield increases at 3 of 6 site-years and 4 of 6 site-years from starter fungicide and R1 foliar fungicide, respectively, when compared to the nontreated due to reduced leaf disease severity. Furthermore, starter fungicide and R1 foliar fungicide increased grain fill duration on average by 3.5 and 4.5 days, respectively, and increased maximum dry kernel weight on average by 5.7% and 9.4%, respectively, across 2022 and 2023. Overall, research data show the ability of various fungicide programs to reduce leaf disease severity, lengthen grain fill duration, and increase final kernel weight, each of which helps explain the yield component mechanisms when foliar diseases are controlled.

The establishment of a global medicinal Cannabis sativa (L.) industry has necessitated improving inflorescence traits, including elements of uniformity, quality, and yield across varying growth conditions for optimum production. To understand the causal mechanisms affecting variation in growth and yield, 20 ∆⁹-tetrahydrocannabinol- and cannabidiol-dominant varieties were used to assess agronomic traits in two different commercial settings. To aid standardization of floral traits in cannabis, we developed a novel phenotyping method using image segmentation to study inflorescence size and compactness. We apply this method to examine the undesirable indeterminate characteristic, “foxtailing,” that presents as elongated calyces and loose inflorescences. Our findings quantified both the genetic and environmental influences affecting expression of agronomic traits in cannabis and further highlight significant genotype-by-environment interactions. The findings suggest possible genetic control of foxtailing, evidenced by multiple varieties displaying the trait in differing environments. By uncovering the interplay between genotype and environment, and shedding light on inflorescence compactness, we provide actionable insights that can inform strategic breeding approaches and unlock the full potential of cannabis cultivation. These findings can help elevate productivity and make significant contributions toward yield optimization within different environments.