Crop Science最新文献

Maize (Zea mays L.) is an important crop globally, and obtaining more productive and resistant commercial cultivars is of paramount importance. In this context, adequate analysis of data from multi-environment trials is essential for the accurate modeling of genotype × environment interaction (GEI), thus providing crucial support for decision-making in plant breeding programs. This study uses a Bayesian analytical factorial model (Bayesian factor analytic [BFA]) to analyze the adaptability and stability of grain yield in a collection of 100 maize hybrids evaluated in 14 representative environments of the Southeast region of Brazil. The aim was to highlight and discuss aspects related to the application of the BFA, addressing the advantages and challenges involved. The goal was to explore the interpretations and limitations of the analysis, in order to assist breeders and researchers in the proper use of the employed method. The results allowed us to identify distinct subgroups of genotypes and environments with similar effects, as well as to identify stable genotypes in relation to GEI and to suggest genotype recommendations for specific environments. To achieve this goal, the flexibility of the BFA model was exploited to incorporate inferences to the various parameters, especially bilinear parameters that describe G + GEI in the biplot.

Public forage variety trials are the only objective source of information for alfalfa producers purchasing seed. There has been extensive research improving the efficiency of individual trials, but limited research guiding the best practices within a network of trials. A retrospective analysis of hundreds of alfalfa (Medicago sativa L.) trials in the US Midwest, Northeast, and Great Plains revealed that individual trials have limited external validity. Based on mean variety yield in a trial, only 36.2% of genetic variance was consistent across many trials. Genotype-by-environment effects explained 26.2% of the variance. Residual variance was confounded with genotype-by-trial variance and explained 37.6%. This resulted in standard errors of 4.1%, 3.3%, and 2.9% (variety deviation from trial mean yield) for varieties evaluated in two, three, and four trials, respectively. Practically, four or five trials are suggested as minimum threshold for a reliable yield estimate in alfalfa. Fortunately, the scale of genotype-by-environment variation is small relative to other crops, suggesting that superior alfalfa varieties are superior across a large geographic range. Last, there was breeding progress for biomass yield (+0.63% per year; standard error = 0.029%; p = 9.0 × 10⁻⁷⁸) in alfalfa between 1988 and 2017.

Genomewide selection is effective if its prediction accuracy (r_MG) is high. The r_MG is known to decrease after several cycles of selection, but a systematic analysis of the factors that contribute to the decline in r_MG has not been reported. My objective was to assess what factors contribute the most to the decay in r_MG during genomewide selection. Ten cycles of genomewide recurrent selection with different genetic models were simulated for a maize (Zea mays L.) biparental cross. In the benchmark model, which involved 250 quantitative trait loci (QTLs), N = 200 plants in each cycle, and the best N_Sel = 10 plants selected in each cycle, the r_MG declined from 0.77 in Cycle 0 to 0.16 in Cycle 10. Results for truncation versus random selection indicated that directional selection itself accounted for >50% of the variation in r_MG. The decay in linkage disequilibrium across cycles of selection accounted for nearly 30% of the variation in r_MG. Genetic drift, number of QTLs, and having functional versus random markers had nonsignificant effects on r_MG. Suppression of crossing-over along with random selection maintained r_MG at 0.76–0.77 across all 10 cycles but, as expected, led to no selection gain. Because selection and a decay in linkage disequilibrium are inherent in genomewide recurrent selection, a decrease in r_MG is an inevitable price to pay for genetic gain. A new prediction model is then needed after several cycles of selection.

Sulfur (S) is a crucial macronutrient for turfgrass growth, and declining atmospheric deposition has increased the risk of S deficiency. Reliable Mehlich-3 soil test interpretations for S are lacking for turfgrass. This study aimed to correlate and calibrate Mehlich-3 S with the visual quality and yield of Kentucky bluegrass (Poa pratensis L.) through four field trials conducted over 2 years in Lexington, KY. Results indicated that critical Mehlich-3 soil S concentrations for acceptable turfgrass quality ranged from 3.1 to 5.7 mg kg⁻¹ varying by seasons, and for maximum yield ranged from 6.1 to 8.1 mg kg⁻¹. Applying S at a rate of 8.2 kg ha⁻¹ alleviated visual S deficiency symptoms and resulted in acceptable turfgrass quality when soil S was limiting. Tissue S content was more variable than extractable soil S but tissue S also correlated well with turfgrass response. When antecedent Mehlich-3 S levels were ≥8.4 mg kg⁻¹, S application did not improve turfgrass parameters. This research demonstrates the Mehlich-3 extract can be used to predict soil S deficiencies for Kentucky bluegrass, with concentrations ≤5.7 mg kg⁻¹ suggesting a high risk of turfgrass quality reduction, and soil with >8.4 mg kg⁻¹ unlikely to be associated with S-deficient Kentucky bluegrass.

Kiesbauer, J., Kölliker, R., Hug, M., Sindelar, M., Schlatter, L. H., Ohnmacht, J., Studer, B., & Grieder, C. (2025). Higher seed yield through selection for reduced seed shattering in Italian ryegrass. Crop Science, 65, e70002. https://doi.org/10.1002/csc2.70002

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Open access publishing facilitated by Agroscope as part of the Wiley-Agroscope agreement via the Consortium of Swiss Academic Libraries.

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Beres, B. L., Wang, Z., Stevenson, F. C., Geddes, C. M., Tidemann, B. D., Kubota, H., May, W. E., & Mohr, R. M. (2025). Optimizing canola production in the Northern Great Plains by leveraging genotype × environment × management synergies. Crop Science, 65, e70115. https://doi.org/10.1002/csc2.70115

The funding statement for this article was missing. The below funding statement has been added to the article:

Open Access funding provided by the Gouvernement du Canada Agriculture et Agroalimentaire Canada library.

We apologize for this error.

Growth regulation in cotton (Gossypium hirsutum L.) is essential for shaping optimal plant architecture and achieving full mechanization in its cultivation. However, the regulators are often poorly controlled when applied individually, leaving plants with poor morphology, which in turn leads to lower yields. Combining regulators is the key to solving these problems. In this study, the effects of spraying 0.1% thidiazuron (TDZ) (T1) individually and TDZ mixtures with 98% mepiquat chloride (T2), 5% prohexadione calcium (T3), and 5% uniconazole (T4), with pure water as a control, were studied. The results showed that the combination of plant growth regulators optimized the cotton plant architecture, and the leaf area index, diffuse not-intercepted area, as well as mean tilt angle of the T4 group increased by 58.4%, 28.1%, and 9.9%, respectively, at the boll-setting period. Net photosynthetic rate, stomatal conductance, intercellular CO₂ concentration, and transpiration rate increased by 27.2%, 20.0%, 6.6%, and 52.4%, respectively, at the peak flowering period. Further, the dry matter accumulation of cotton in reproductive organs increased by 9.6% and 14.3% for 2 years, respectively, and the average seed cotton yield of 2 years was significantly increased by 12.5% under this treatment condition. This study revealed that the combination of 0.1% TDZ and 5% uniconazole could improve the photosynthetic capacity of cotton plants and increase dry matter accumulation through optimizing plant architecture, thus increasing the seed cotton yield. In conclusion, it provides technical guidance for suitable plant architecture and high-yield cotton cultivation in Xinjiang.

The north-eastern region of India, being considered part of the Indo-Malayan biodiversity hotspot, suffered from the extinction of a large number of landraces including rice (Oryza sativa L.). Recent changes, specifically rapid urbanization, extreme climate events, and the introduction of profitable agriculture using high-yielding varieties, have also supplemented the process. Systematic evaluation of diverse genotypes is urgently required for their proper utilization. By evaluating 148 rice genotypes from the north-eastern hilly region of India over three successive years, key traits—namely, the number of filled grains per panicle, test weight, and yield per plant were identified. These traits were found to be governed by additive gene action, with lesser environmental effects on their expression. Based on the multi-trait stability index and GGE (genotype main effect plus genotype by environment interaction) biplot analysis, eight genotypes, namely, Beoidhan 2, Jalbudi, Motadhan, Salidhan, Tapolea, Lypyagopal, Badalsali, and Dagum, were identified. The first five principal components cumulatively explained 79.51% of the total variance. Genotyping of rice germplasm using 50 Generation Challenge Programme markers resulted in a total of 94 alleles. Polymorphic information content ranged from 0.14 to 0.69 with an average of 0.36. Likewise, Shannon's information index ranged from 0.20 to 1.33, with an average of 0.57. Nei's genetic distance-based clustering has grouped the genotypes into four major clusters, whereas the Bayesian model-based approach has resulted in two groups with 142 pure lines and 06 admixtures. Based on an analysis of molecular variance, 57.63% of the variance was due to genetic differentiation among the individuals within populations, while 41.92% of the variance was accounted for within individuals. Wright's statistics indicated that the genotypes of Sikkim were highly differentiated from those of Tripura. The second-highest level of differentiation was observed among the genotypes of Mizoram and Tripura. In principal coordinate analysis, the first three axes explained 25.79% of the total variation. The landraces with a high level of differentiation and enhanced yield potential in multi-environments, namely, Motadhan, Salidhan, Tapolea, and Lypyagopal, would help increase the yield potential vis-à-vis farmers' income.

Genome-wide association studies (GWAS) have become a pivotal tool in identifying marker-trait associations (MTAs), thus ultimately facilitating the improvement of desirable traits like disease resistance in plants. However, the introduction of new alleles poses challenges due to the potential co-integration of undesirable traits. This study builds upon the findings of MTAs for sclerotinia stem rot (SSR) resistance in soybeans (Glycine Willd.) that we reported previously. By employing the same soybean genetic diversity GWAS panel used in the previous study, we performed a set of genomic analyses to examine any potential linkage drag. This was performed through GWAS that aimed to explore the co-localization of MTAs associated with SSR resistance so as to assess the effects of resistance alleles on both agronomic and seed quality traits. Of the 29 MTAs identified in this study, only seven protein-related MTAs shared a chromosome with the previously identified SSR resistance MTA. In addition, there seems to be no yield penalty for the partially resistant soybean genotypes. To the contrary, in certain instances, an advantage was associated with carrying SSR resistance alleles concerning the agronomic and seed quality traits. While these findings are promising, they should be considered preliminary and warrant further investigation. We anticipate that these results will provide a solid foundation for studying the potential effects of SSR resistance alleles on other desirable traits in soybean.

The incorporation of perennial groundcover crops (PGCC) into cotton (Gossypium hirsutum L.) systems may provide similar benefits to annual cover crops with fewer associated costs, but their effects on boll production and fiber quality have not been investigated. This study assessed the effects of growing PGCC species on end-of-season boll formation, retention, position, distribution, and fiber quality compared to annual cover crops or fallow systems. Over 2 years, cotton was interseeded and grown in four cover crop treatments as follows: (1) terminated weedy fallow, (2) terminated annual ryegrass (Lolium multiflorum Lam.), (3) a living 1:1 mix of perennial red clover (Trifolium pratense L.) and white clover (Trifolium repens L.), and (4) a living 2:1:1 mix of ryegrass, red clover, and white clover. Post-harvest plant mapping was conducted on plants from outside of harvested rows in each plot to determine final boll set, position, and canopy distribution as well as sympodial and monopodial branching. Fiber quality was also assessed via high volume instrument. Results indicated that the presence of clovers increased boll density lower in the canopy while also increasing the number of position 2 bolls compared to the fallow system, and that growing perennial clovers alongside cotton reduced formation of monopodial shoots. Presence of perennial clovers also reduced the incidence of short fibers in cotton under drought conditions, but did not alter any other fiber quality parameters over the study period. These findings further support the use of PGCC as a mechanism to improve adoption of cover crops in cotton production.