Crop Science最新文献

Genomic selection (GS) is a powerful strategy for accelerating genetic gain in plant breeding. While in recent years GS has been widely adopted in breeding programs for agronomic crops, its implementation in vegetable breeding has been comparatively limited. Vegetable breeders face many unique challenges that impede the direct translation of GS implementation strategies from agronomic breeding programs. These challenges include the large number of traits that are important for cultivar development, the difficulty in quantitatively phenotyping many of these traits, especially those related to quality and sensory attributes, and the diversity of reproductive strategies and biological features represented among different vegetable crops. Successful vegetable breeders have been able to efficiently develop new varieties with improved quality and productivity, while constantly adapting to shifting market demands and growing methods, by complementing their understanding of heredity with elements of creativity and intuition-based decision-making. Like earlier advances in genetics and statistics that were once viewed as only theoretical, we feel GS can become an additional part of breeders’ routine selection strategy and, ultimately, another element of the “art” of vegetable breeding.

African yam bean (AYB), or sphenostylis stenocarpa (Hochst. Ex. A. Rich) Harms, is a leguminous crop with potential to enhance food security and agricultural sustainability. A total of one hundred Africa yam bean accessions from six Nigeria states were planted for agronomic evaluation. Out of 100 samples, ninety-four accessions were genotyped using DArTseq approach and generated 2527 high-quality single-nucleotide polymorphism (SNP) polymorphic markers. The QC-filtered markers had a call rate ≥0.80, marker reproducibility ≥0.95, minor allele frequency ≤0.01, and missing data ≤20%. The expected heterozygosity varied from 0.007 to 0.201 while the observed heterozygosity varied from 0.015 to 0.121. The overall inbreeding coefficient (FIS) was 0.623. The results revealed diversity within the AYB accessions for both the agronomic trait and SNP markers. Analysis of variance revealed significant variations (p ≤ 0.05) in traits such as total seed weight, days to first flower, and pod length (PODL), suggesting genetic diversity within the population. Tropical Sphenostylis stenocarpa (TSs) accessions TSs-513, TSs-560, TSs-526, TSs-571, TSs-581, TSs-601, TSs-602, and TSs-582 exhibited excellent performance for some traits such as number of seeds per pod, PODL, and days to first flowering. Neighbor-joining cluster analysis grouped the AYB population into four main clusters, where majority of Abia, Enugu, and Cross River states AYB accessions were grouped together with their origin. Population structure analysis results were consistent with the cluster analysis. The comprehensive view of genetic diversity and population structure analysis, highlights both genetic distinctness between geographical origin and relationship among accessions. The results of genetic diversity and population structure analysis confirmed that there is substantial genetic variation among the AYB accessions. These results provide valuable insights for AYB breeding in sub-Saharan Africa, enabling the selection of diverse parental lines, maintaining genetic variability, and enhancing adaptability. Understanding genetic structure enables efficient germplasm conservation and the development of improved, resilient breeding populations.

Accurate simulation of the crop growth process was the foundation for the development of smart agriculture. However, the uncertainty of crop growth models limits their practical application. This study integrates the Soil Water Atmosphere Plant (SWAP) model with the Iterative Ensemble Smoother (IES) algorithm to develop the SWAP–IES optimization approach and explores various uncertainty factors of the system, including the ensemble size, observational errors setting, combination of observation variables and their corresponding observation stages, and uncertain parameters selection. The results suggested that, under water stress conditions, an ensemble size of 50 was recommended. It was advisable to choose leaf area index (LAI) and soil moisture content (SW) as observation variables, focusing on monitoring data from the flowering to the milk stage. The suitable observational error settings for LAI and SW were 0.3–0.5 m² m⁻² and 0.03–0.05 cm³ cm⁻³, respectively. For uncertain parameters, it was recommended to select the five crop parameters (RGRLAI, SPAN, CVO, EFF, and CVL) and three soil parameters (θ_s, K_s, and n) for simulation. The SWAP-IES, validated with 2020 and 2021 spring wheat (Triticum aestivum L.) experiments, demonstrated high accuracy in simulating yields, with root mean square error values of 0.56 and 0.61 t ha⁻¹, respectively. The SWAP–IES optimization approach could significantly reduce the uncertainty in the simulation process and improve simulation accuracy by optimizing the system settings strategy.

Farmers often use private and public labs, crop advisors, or fertilizer dealers to determine fertilizer needs for crops, with recommendations and resulting costs from these sources having the potential to vary greatly. Twelve on-farm trials across the state of Utah in alfalfa (Medicago sativa), small grain forage, and silage corn (Zea mays) were established in 2021 to compare fertilizer recommendations from five labs and a nonfertilized control, two public labs (Utah State University and University of Idaho), and three commercial labs in the Western United States, with some sites being replicated in 2022–2023. A baseline soil sample from each field was split and sent to multiple labs for analysis and corresponding nutrient rates recommended by each lab applied at each site. Fertilizer recommendations from the five laboratories varied greatly, both for types of nutrients and rates recommended, with differences between highest and lowest treatment costs ranging from $528 to $2024 ha⁻¹ across sites. Crop yield and forage quality data were collected from sites from 2021 to 2023, with fertilizer treatments having little to no impact at four silage corn or five alfalfa sites. Yield was increased by at least one private and university lab at all three small grain forage sites and crude protein content was increased at sites with multiple years of data. Fertilizer treatments occasionally improved forage yield and quality but not crop market value. The results of this study demonstrate that growers should be aware when selecting fertilizer recommendations, and opportunities exist for better public-private coordination of science-based recommendations.

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.

The native, perennial shrub American hazelnut (Corylus americana) is cultivated in the US Midwest for its significant ecological benefits, as well as its high-value nut crop. Genetic improvement of perennial crops involves long-term breeding efforts, and benefits from the use of genetic data in selection to reduce breeding cycle time. In addition, high-throughput phenotyping methods are essential to the efficient and accurate screening of large breeding populations. This study reports novel advances in both of these domains, for American (C. americana) and interspecific hybrids between European (Corylus avellana) and American hazelnuts. Two populations of hazelnuts, one composed of C. americana and one composed of C. americana × C. avellana hybrids, were phenotyped over the course of 2 years in two locations using a digital imagery-based method for quantifying morphological nut and kernel traits. These data were used to perform composite interval mapping using a recently released genetic map, and genomic prediction using a newly available chromosome-scale reference genome for C. americana. Multiple quantitative trait loci were detected for all traits analyzed, with an average total R² of 52%. Genomic prediction exhibited high accuracy, with an average correlation coefficient between genotypic values and phenotypic observations of 0.78 across both environments. These results suggest that incorporating genetic data in selection is a tenable method for improving genetic gain for highly polygenic traits in hazelnut breeding programs.

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.