Crop Science最新文献

Frequently occurring extreme weather events and environmental changes may significantly reduce corn (Zea mays L.) yields. Thus, the selection of favorable traits and stable genotypes has emerged as a fundamental objective of breeding programs aimed at countering adverse weather effects. Field experiments in eight environments were conducted in 2019 and 2020 to evaluate the performance and stability of 93 inbred maize lines by multiple models and parameters. The genotype–environment interaction (GEI) plot and GEI effect functions in the Metan package were used to visualize the response patterns of different genotypes in multiple environments. Response patterns of 93 inbred lines with different traits across eight environments were constructed, revealing substantial GEI for anthesis–silking interval, days to 50% anthesis, and days to 50% silking, which were primarily influenced by environmental factors. Through evaluation by multiple methods, a total of 13 genotypes demonstrated excellent performance across four or more parameters or models, such as Zong31, Xz5426, and so forth. Based on the multi-trait stability index (MTSI) model, all traits were positively selected. Grain yield had the highest selection weight at 25.8%, while ear barren tip had the lowest at 6.19%. Thirteen genotypes were selected, with DH509-9 being the most stable (MTSI = 3.75). Cross-validation revealed superior predictive accuracy in all additive main effects and multiplicative interaction (AMMI) models compared to best linear unbiased prediction (BLUP) models. The mean root mean square prediction difference was highest for AMMI0 (72.06) and lowest for BLUP_e (27.08), and AMMI0 model was the optimal model. The approach investigated in this research has the potential to significantly streamline the decision-making process for breeders to identify genotypes characterized by both high average performance and robust phenotypic stability.

In the context of over-fertilization, especially of phosphorus (P), the debate about the usefulness of applying starter fertilization to maize (Zea mays L.) must be revisited. One solution is to breed crops with an enhanced phosphorus use efficiency, which require less fertilizer yet are high-yielding. This study examined a diverse panel of Flint elite lines and double haploid lines from six European landraces, which were crossed with two Dent testers. The resulting 588 testcross hybrids were evaluated under two fertilization treatments: with and without the addition of a di-ammonium phosphate starter fertilization. The omission of the starter fertilization led to a decrease in early developmental traits, like plant height and biomass, in all four tested environments. Surprisingly, grain yield increased in three out of four environments, an effect that was especially noticeable in the landrace line testcrosses and is possibly caused by the increased ability to cope with environmental stress occurring at later developmental stages. Importantly, there is substantial genetic variation that can be exploited in breeding for the response to fertilizer levels, with some landrace testcrosses performing in the range of the Flint elite testcrosses. Furthermore, additive genetic effects were found to be the main contributor to early developmental traits and grain yield under both fertilization treatments. These results suggest that landraces may offer valuable genetic variation for breeding for reduced phosphate fertilizer input. In conclusion, breeding programs should include breeding for nutrient acquisition but combined with a tolerance to withstand seasonal climate variations.

Numerous activities in the plant sciences require time-consuming, repetitive actions that are ideal for automation, but existing tools to accomplish these types of tasks are often priced beyond the reach of many research labs, especially in low-resource environments. We developed a suite of easy-to-use, three-dimensional (3D)-printable tools for seed handling, tissue collection, and bead dispensing. The designs were made using accessible software and tested for speed and accuracy across multiple crops. Compared to commercial and manual methods, the 3D-printed tools were significantly faster with comparable or superior accuracy. Costs of printed tools were 0.1%–21% of commercial equivalents. All designs are freely available online and can be easily adjusted to suit different research needs or printer types. Inexpensive, open-source hardware can meaningfully increase throughput, standardization, and reproducibility in plant research, especially for programs operating under budget constraints.

Proteomics enables the comprehensive analysis of proteins that drive cells and is thus the ultimate method for profiling biological systems. However, proteomics analyses are time-consuming and expensive, which has limited their applications to basic science and advanced medical research. The recent development of technologies enabling the generalization of proteomics has led to its application in new fields. Artificial intelligence (AI) is particularly useful for mining of proteomics data to yield new knowledge, as it allows for the integration of a wide variety of metadata—information considered necessary to explain experimental data. Recent improvements in the capabilities of AI have facilitated the practical and social implementation of proteomics. In this review, we describe how AI proteomics has expanded the scope of biological testing and discuss its potential and prospects for applications in agriculture. The potential of AI proteomics to provide detailed information on the state of seed germination and dormancy is discussed. In addition, we will discuss results of an investigation into barley leaves using high-throughput proteomics technology, which is the fundamental technology of AI proteomics. In the future, increasing the amount of data and analyzing it with AI is likely to yield insights that were not previously available. Furthermore, the introduction of this technology into the field is expected to enable more accurate and effective crop management.

The escalating issue of global soil salinization has significantly impacted the growth and productivity of rice (Oryza sativa L). To investigate the mechanisms underlying rice seedlings’ response to salt stress, transcriptome analysis to examine gene expression changes in salt-tolerant and salt-sensitive rice varieties was conducted. Salt-tolerant landrace rice, Faguodao, and salt-sensitive cultivar rice, Nipponbare, were used in this study. Both were subjected to 125 mM NaCl treatment at the seedling stage, and transcriptome sequencing was employed to analyze stress-responsive genes and regulatory networks. Differentially expressed genes in both rice varieties under salt stress were enriched in the abscisic acid (ABA) and jasmonic acid (JA) hormone signaling pathways. Key genes such as OsABIL1 (ABA signaling component) and OsJAZ11 (JA pathway repressor) were identified as pivotal regulators. OsABIL1 promoted ion homeostasis under salt stress, while OsJAZ11 suppression indicated JA signaling inhibition, highlighting ABA's dominance in salt tolerance. Exogenous ABA application significantly alleviated salt stress damage in both genotypes by modulating ion homeostasis, whereas exogenous JA suppressed ABA-responsive gene expression (e.g., OsPYL4 and OsbZIP23), indicating an antagonistic interaction between the two hormones. Under salt stress, exogenous ABA effectively alleviates the damage in both salt-tolerant and salt-sensitive rice. In contrast, exogenous JA suppressed the expression of ABA-related genes, diminishing ABA's alleviating effects and indicating an antagonistic interaction between ABA and JA in regulating rice salt tolerance. This study provides valuable insights into the intricate regulatory network of ABA and JA regulating salt tolerance in rice seedlings.

The re-emergence of wheat stem rust disease, caused by Puccinia graminis Pers. f. sp. tritici (Eriks and E. Henn.) (Pgt), has recently been reported in Europe and North Africa. The prevalence of virulent Pgt genetic groups in Mediterranean basin countries, combined with the limited number of characterized resistance sources in durum wheat (Triticum turgidum L., ssp. durum (Desf.) Husn.) germplasm, poses a serious threat to durum wheat production. In this study, we evaluated a collection of Mediterranean wheat accessions, mainly durum wheat, for seedling stage resistance to two Ug99 races (TTKTT and TTKSK) and the recently emerged and prevalent races TTRTF (aka Sicily race) and TKKTF. Although 24% of the genotypes exhibited resistant responses, phenotyping screening showed significant variation in seedling responses to the different races. Specifically, 27% and 30% of the genotypes were resistant to TTRTF and TTKSK, whereas only 17.5% and 18% exhibited resistance to TKKTF and TTKTT, respectively. Only 9.4% of genotypes (n = 13) exhibited resistance or intermediate responses to all four tested races. Wheat accessions from Portugal, France, and Spain showed the highest resistance frequencies, ranging from 30% to 50%. Molecular analysis revealed the presence of the resistance gene Sr13 in 13 genotypes, eight of which originated from Tunisia (one landrace and seven varieties). The study demonstrates the importance of Mediterranean durum wheat accessions as sources of novel and diverse genetic resistance to the predominant races in the Mediterranean region, especially to the TTRTF, TKKTF, and Ug99 lineage races.

Genetic gain is an annualized measure of trait improvement used to estimate plant breeding progress, wherein the performances of old versus new variety releases are compared over time. Herein, we present data from the United States Department of Agriculture-coordinated hard winter wheat (Triticum aestivum L.) Southern regional performance nursery (SRPN) collected from 1959 to 2024 for grain yield, grain volume weight, days-to-heading, and plant height and use it to estimate absolute and relative rates of genetic gain (loss) of the control cultivar Kharkof for winter wheats adapted to the Southern Great Plains of North America. Regression analyses revealed significant relative grain yield increases of 0.90% Kharkof year⁻¹ and 24.4 kg ha⁻¹ year⁻¹ for the SRPN over the 66-year period. Notwithstanding, nonlinear statistical modeling of SRPN relative and absolute grain yield datasets provided the most parsimonious fit of the data, revealing highly significant linear breakpoints (i.e., plateaus) beginning in 1998 for relative %Kharkof yield, with quadratic models best explaining absolute (kg ha⁻¹ year⁻¹) yield data for the region, suggesting a more gradual leveling off of grain yield over time instead of an abrupt breakpoint. Trends for both relative and absolute grain yield of 0.57% Kharkof year⁻¹ and 15.4 kg ha⁻¹ year⁻¹, respectively, since 1984 further demonstrate reduced annualized gains in comparison to the 66-year trendlines. These broad-sense, proxy estimates of genetic gain reported herein reconfirm that wheat breeding progress remains difficult in many marginal production environments constituting the US Southern Plains, but the parabolic best-fit curve for absolute kg ha⁻¹ year⁻¹ grain yield of 5MP and all SRPN entries indicates that yield gains are still moderately increasing.