Crop Science最新文献

It is essential to evaluate the genetic gain of lint yield in modern cotton (Gossypium hirsutum L.) cultivars planted in recent history and identify the trend of potential changes due to changes in breeding priorities in the United States. In National Cotton Variety Tests (NCVT) conducted since the 1960s, Upland cotton cultivars were tested annually at locations across the US Cotton Belt. The NCVT data from 1998 to 2022 after commercialization and inclusion of transgenic cotton cultivars were used to analyze lint yield trends during this period. The annual yield means were adjusted based on overlapped entries between testing years to minimize environmental influence during the long-term trials for genetic gain, which was estimated from regression of the adjusted annual means over testing years. The results showed that genetic gain of lint yield was 24.1 kg ha⁻¹ year⁻¹ during the 25-year period. When the long period was split into two segments, that is, 1998 to 2014 and 2015 to 2022, the genetic gains were 24.7 kg ha⁻¹ year⁻¹ and −1.3 kg ha⁻¹ year⁻¹, respectively. The yield trend of increasing before 2015 and plateauing after 2015 coincides with the trend of stacking technology advancement in development of transgenic cultivars. This coincidence reflects the early success of stacking technologies by seed companies in pyramiding stacked genes with cotton yield during the 2000s and the middle of 2010s. The yield plateau suggests the necessity of breakthroughs in breeding methods and biotechnologies in development of transgenic cotton for further increasing yield.

Roots are critical for supporting basic plant functions such as anchoring in various substrates, uptake of water and nutrients, and hosting symbiotic relationships. In crops, indirect changes to root system architecture (RSA) have occurred largely as a result of selection for yield or other related aboveground traits. In cultivated soybean (Glycine max (L.) Merr.), evidence of changes to RSA resulting from breeding for crop performance has been inconsistent, with some studies supporting an overall decrease in performance-related trait values, such as root length and density, and other work showing the opposite. The current study sets out to ask whether there is any systematic differentiation in RSA between a set of elite breeding lines (n = 8) of soybean developed for the Midwest United States and a group of diversity lines from the USDA Soybean Germplasm Collection (n = 16). Groups are compared across three distinct developmental stages (V2–V6, V7–R2, and R3–R7) and two contrasting soil environments. In total, 432 root systems were phenotyped for 12 structural traits derived from two-dimensional images along with root and shoot biomass. A new three-dimensional root modeling approach leveraging photogrammetry-derived pointclouds is additionally tested on a subset of 30 contrasting root systems. Results indicate that the diversity lines had smaller root systems overall but greater phenotypic plasticity in response to soil environment as compared to breeding lines. Plants grown in clay loam soil had reduced taproot length (14.2%), root biomass (18%), root volume (22.9%), root spread (22.7%), and average root diameter (7.6%) compared to sandy loam soil. In addition, root traits showed generally low heritabilities. Overall mean heritabilities were found to be highest in the earlier timepoint and declined over time. Maximum taproot diameter (H² = 0.37 and h² = 0.21) and maximum lateral branch length (H² = 0.22 and h² = 0.13) were the most heritable traits. Furthermore, the study finds evidence for trade-offs between aboveground and belowground trait plasticity.

Identifying superior cassava (Manihot esculenta Crantz) crosses through phenotypic evaluations is costly and inefficient due to cassava's long breeding cycles and low flowering rates. In this study, we applied genomic mate selection and optimum contribution selection using additive, dominance, and directional dominance models to enhance prediction accuracy and optimize cross design in cassava. A total of 3391 clones were evaluated across 49 multi-environment trials for fresh root and shoot yield, dry matter, starch content, and plant architecture. Directional dominance effects improved predictive ability for all traits except starch, emphasizing the role of nonadditive effects in cassava breeding. Genomic mating enhanced predicted gains for yield and quality traits, while reducing predicted values for plant architecture, aligning with selection for compact ideotypes. Although optimum contribution selection effectively controlled inbreeding, it reduced the number of selected crosses, reflecting a trade-off between diversity and gain. Parent-wise cross-validation confirmed that directional dominance models consistently produced higher predicted means for fresh root and shoot yield and dry matter content. The most promising crosses were identified based on a multi-trait selection index and usefulness criteria, integrating mean performance and within-family variance. Our results demonstrate that combining directional dominance modeling with genomic mating tools increases breeding efficiency, identifies crosses with superior predicted performance, and supports ideotype-based breeding. This strategy offers a cost-effective approach for accelerating genetic gain while maintaining diversity in cassava improvement programs.

Turfgrass species mixtures are often recommended over the use of single species due to greater genetic diversity to meet broader landscape needs. However, the intended composition of the mixture can change over time due to rates, contrasting tolerance to environmental stresses, and management practices. Strategies used to increase tolerance to stresses, such as drought, include applications of beneficial microorganisms, which may favor some turfgrass species in a mixture. The application of mycorrhizal inoculant is popular, but mycorrhizae's impact on turfgrass mixture response to drought is unknown. To address both the need for more information about turfgrass mixtures and the use of microbial inoculants, field experiments were conducted in Minnesota using mixtures and monocultures of Kentucky bluegrass (Poa pratensis L.), perennial ryegrass (Lolium perenne L.), and hard fescue (Festuca brevipila Tracey), each with and without inoculation with mycorrhizae. Plots were exposed to sequential drought and recovery periods lasting ∼30 days. Data were collected on turfgrass health and species cover. Results showed that the application of mycorrhizal inoculant during the establishment period did not impact species cover and had little effect on reducing symptoms of drought stress. Hard fescue performed the best during both drought and recovery even when mixed at a low proportion with the other species, especially when mixed with Kentucky bluegrass. Turfgrass species cover was consistent across drought and recovery periods, except for when species were replaced by bare soils or weeds.

Bourdoncle, W., Lemke, B., Maloney, P., Pellet, J.-L., Zhao, J., Fouquet, R., Cargill, E., & Barten, T. (2023). The brachytic2 mutation alone or its combination with the brown midrib3 mutation improves fiber digestibility in forage maize. Crop Science, 63, 2856–2864. https://doi.org/10.1002/csc2.21078

This erratum corrects the following error:

The data in the last column of Table 1, labeled “Milk (kg milk Mg⁻¹ DM)” were mistakenly given in lbs per US ton of DM and not the metric units:

This has now been updated to include the correct metric units in the “Milk (kg milk Mg⁻¹ DM)” column:

We apologize for this error.

Phytic acid (PA) is the primary storage form of phosphorus in seeds and is considered an anti-nutritional factor because of its ability to chelate essential minerals, thereby reducing their bioavailability. However, identifying low-PA mutants in large populations requires a cost-effective, accurate, and high-throughput screening method. A previously reported colorimetric method for quantifying PA in soybean (Glycine max (L.) Merr.). However, the throughput of that method is relatively low. In this study, we modified several key steps of the protocol to improve its throughput. The accuracy of the modified protocol was validated by comparing it with the original method and a commercially available PA quantification kit. The new high-throughput protocol showed high reproducibility and successfully distinguished existing low-PA mutants from their wild-type parent. The protocol was then used to screen a diversity panel of 202 pea accessions (Pisum sativum L.), which revealed a wide genetic variation in PA content. We identified two novel low-PA accessions, JI0383 and JI3253, with 69% and 48% reductions in PA, respectively, compared to the population mean values. This cost-effective method is expected to help researchers and breeders accelerate the development of low-PA crops to meet the current demand for high-quality plant-based foods.

Pea is a model organism in biology and an agronomic crop, entering the genomics era. Although RNA-sequencing (RNA-seq) has become the main transcriptomic approach in biology, there is currently no genome-wide expression atlas covering a wide range of biological conditions for this species. Here, we generate a transcriptomic atlas in Pisum sativum by integrating 149 publicly available RNA-seq libraries, covering 10 cultivars, and a comprehensive collection of plant organs and several environmental stress conditions (heat, low temperature, nutrient, and water deficit). As proof of concept, we first verified the expression profiles of key gene families, such as sugar transporters and transcription factors (TF), across this transcriptomic atlas. Using a systems biology approach, we then inferred a regulatory network of genes responsive to water deficit, from which we predicted putative TF-target interactions, including genes encoding monosaccharide transporter PsSTP13.2 and sugar facilitator PsSWEET6. Finally, we exploited our meta-analysis to identify new reference genes with stable expression based on their coefficient of variation. Ten reference genes were validated by quantitative PCR across various biological samples, including different pea varieties, organs, and stress conditions (water deficit and fungal pathogen infection). Three reference genes (Psat6g102320, Psat6g163160, and Psat7g253080) outperformed the expression stability of common housekeeping genes (TFIIA, PPIIA, and β-tubulin). Altogether, this atlas opens new avenues of integrative research in the genomics and systems biology era of legume crops.

Root and tuber crops are a good source of food energy on a global scale, particularly in African and Asian nations. Nevertheless, these crops can be susceptible to abiotic stresses such as excessive heat, high salinity, drought, and nutrient deficiencies, which have detrimental effects on the physiological and metabolic processes of the plants, severely decreasing their yield. These abiotic stresses induce alterations in molecular structures, particularly at a protein level. With the release of the genome sequence and assembly of many root and tuber crops such as potato, cassava, beet, yam, taro, and sweet potato, opportunities for improvement of these crops against different abiotic factors have become possible. This review describes advances in the use of proteomics tools in understanding the response of root and tuber crops to abiotic stress, in order to provide insight for breeding strategies. These advancements can boost crop yields, increase agricultural productivity, and enhance global food security.

Sorghum [Sorghum bicolor (L.) Moench] is the fifth most important cereal grain crop worldwide and is used as both feed and food grain. While grain composition and quality are important, they have traditionally been a lower priority relative to grain yield. If methods to predict composition and quality are available, this could be added to the selection criteria with minimal addition of time or cost. Herein, the genetic inheritance of sorghum grain quality traits was assessed in hybrids obtained by crossing 10 elite inbreds from Texas A&M and Kansas State University following factorial mating designs. Grain samples from these 100 hybrids were collected from 10 evaluation environments and then analyzed for starch, protein, fat, and fiber using near-infrared spectroscopy. In addition, grain samples were characterized for three physical factors: kernel hardness index (KHI), kernel diameter (KD), and kernel weight (KW). Environmental effects were a major source of variation for starch (33.9%), fat (53.5%), and fiber (53.9%), whereas genetic effects were prominent for protein (29.9%), KHI (59.7%), KD (56.9%), and KW (43.8%). Starch, fiber, KHI, and KD predictions were more accurate (0.38–0.74) than those for protein, fat, and KW (0.26–0.70). Finally, multi-trait genomic selection models that included grain yield and days to anthesis improved prediction accuracies up to 18% for grain quality traits over single-trait models. In conclusion, these genomic selection models have the potential to effectively and concurrently select for grain composition and quality factors in sorghum.

Plant growth and development require tightly regulated concentration of heavy metal ions, which function as essential nutrients. In this context, the P1B-heavy metal ATPase (HMA), also known as the HMA family, is critical for mediating metal ion uptake, root-to-shoot translocation, and vacuolar sequestration in plants. This study presents a comprehensive characterization of 11 HMA genes in Phaseolus vulgaris L. (PvHMA). Phylogenetic analysis classified the PvHMAs genes into six distinct clusters, supported by conserved gene structure and motif distributions. PvHMA1, -2, -5, -6, -7, and -11 exhibited widespread expression across multiple tissues and harbored a diverse array of cis-regulatory elements in their promoter, suggesting multiple roles in plant growth and development. In contrast, PvHMA3 and PvHMA8 displayed tissue-specific expression patterns, being predominantly expressed in roots and leaves, respectively. Under zinc stress, PvHMA1, localized in chloroplasts, showed marked upregulation in shoot tissue. Notably, this transcriptional response was not observed under copper exposure, despite the high structural similarity between PvHMA1 and its Arabidopsis thaliana homolog, AtHMA1 (where AtHMA is Arabidopsis thaliana HMA). PvHMA2, an ortholog of the A. thaliana HMA2/4, exhibited increased sensitivity to cobalt stress. Additionally, PvHMA5 and -11 were differentially expressed in shoots in response to zinc treatment. Collectively, these findings provide a detailed overview of the HMA family in P. vulgaris and reveal a complex regulatory network of transporters involved in heavy metal homeostasis, with implications for plant nutrition, development, and stress responses.