Physiological and management factors contributing to soybean potential yield

Roekel, Ryan J. Van; Purcell, Larry C.; Salmerón, Montserrat

doi:10.1016/j.fcr.2015.05.018

Cited by 68 publications

(49 citation statements)

References 131 publications

(223 reference statements)

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“…The expected gain was reduced for the SW and SY traits under a milder selection intensity, while the opposite occurred for the EFP variable. The lower genetic gains for EPF observed in this study can be explained by the high genotype × interaction effect (Table 1), corroborating Van Roekel, Purcell, and Salmerón (2015), who conducted a review on the increase in the soybean yield potential. Similar findings were described by Pereira et al (2017), who selected progeny or lines of soybean for the variables absolute maturity and grain yield.…”

Section: Discussionsupporting

confidence: 88%

Selection of inbred soybean progeny (Glycine max): an approach with population effect

et al. 2018

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To meet the growing demand of the soybean consumer market, cultivars increasingly early, productive and resistant to biotic and abiotic stress are sought. Several populations are obtained in soybean breeding programmes, but progeny are selected without being weighted for their respective population effect. As a consequence, progeny originating from high-merit populations may be discarded too early. Given this scenario, this study proposes to employ the selection index with progeny and population effect via best linear unbiased prediction (SIPP-BLUP) for the genetic selection of early and productive soybean progeny. A total of 180 progeny derived from three populations were evaluated for yield-related traits. Genetic gains from selection, Spearman correlation and coincidence index were used to check the efficiency of the models with and without the population effect. The SIPP-BLUP index achieved greater selection accuracy and was efficient in the identification and future selection of early soybean progeny. Therefore, this study demonstrates that soybean breeding programmes should consider the population effect via SIPP-BLUP in progeny selection to obtain future lines that really contribute to genetic gain. K E Y W O R D Saccuracy, breeding values, earlier-maturing progeny, genetic gain, genotype × environment interaction, mixed models, soybean seed yield

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Section: Discussionsupporting

confidence: 88%

Selection of inbred soybean progeny (Glycine max): an approach with population effect

et al. 2018

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“…These findings suggested that for Enrei, at least, extension of the period from R1 to R3 contributed to the increased pod number, which in turn increased the seed yield. It was widely accepted that the critical period for determination of pods and seeds in soybean is R1 to the beginning of seed filling (R5) period (e.g., Van Roekel, Purcell, & Salmerón, ). Nakano et al () showed that the duration from R1 to R5 also increased by 8 to 10 days with increasing growth temperature in Enrei at the same TGC experiment.…”

Section: Discussionmentioning

confidence: 99%

Is the yield change due to warming affected by photoperiod sensitivity? Effects of the soybean E4 locus

Kumagai

Yamada

Hasegawa

2019

Food and Energy Security

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Modeling studies predict that global warming will shorten growth duration and reduce seed yield of early‐maturing soybean (Glycine max) cultivars, but that late‐maturing cultivars could mitigate this reduction. This is widely discussed but has not been validated experimentally. Time of soybean maturation is determined by several photoperiod‐sensitive loci. Here, we focused on the E4 locus, and tested the hypothesis that this locus would mitigate the growth period shortening and yield reduction due to warming. We sowed cv. Enrei with the dominant E4 allele and a near‐isogenic line with recessive e4 allele (NIL‐e4) on two dates (normal vs. late, with a shorter photoperiod) and grown under three temperature regimes (near‐ambient, and 2.0 and 4.6°C above ambient) in sunlit greenhouses in a cool region of Japan. The period from sowing to flowering (R1) decreased with increasing temperature, regardless of genotype and sowing date. However, increased temperature prolonged the period from R1 to the beginning of pod filling (R3) in Enrei but not in NIL‐e4 for either sowing date. This indicates that increasing temperature shortened the period before R1, exposing Enrei with E4 to a longer photoperiod and therefore slowing its development. For both sowing dates, pod number and seed yield increased with warming in Enrei. Since the days from R1 to R3 and the cumulative radiation for this period were positively correlated with pod number in Enrei, the greater yield response was explained mostly by the prolongation of this period caused by warming. However, the yield increase resulted partially from the current mean growing season temperature, which was near or below the optimum for yield. We conclude that the E4 locus can increase seed yield under future warming in cool regions of Japan. This result demonstrates the potential importance of modifying photoperiod sensitivity to increase soybean yield under future warming.

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“…Crop productivity is a quantitative trait determined by a variety of factors (Van Roekel et al, 2015). Abiotic soil characteristics such as water and nitrogen availability are well-known for being productivity-limiting factors (Durán et al, 2014), and weather conditions such as rainfall or temperature may significantly impact on crop productivity.…”

Section: Discussionmentioning

confidence: 99%

Metagenome-Wide Association Study and Machine Learning Prediction of Bulk Soil Microbiome and Crop Productivity

et al. 2017

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Areas within an agricultural field in the same season often differ in crop productivity despite having the same cropping history, crop genotype, and management practices. One hypothesis is that abiotic or biotic factors in the soils differ between areas resulting in these productivity differences. In this study, bulk soil samples collected from a high and a low productivity area from within six agronomic fields in Illinois were quantified for abiotic and biotic characteristics. Extracted DNA from these bulk soil samples were shotgun sequenced. While logistic regression analyses resulted in no significant association between crop productivity and the 26 soil characteristics, principal coordinate analysis and constrained correspondence analysis showed crop productivity explained a major proportion of the taxa variance in the bulk soil microbiome. Metagenome-wide association studies (MWAS) identified more Bradyrhizodium and Gammaproteobacteria in higher productivity areas and more Actinobacteria, Ascomycota, Planctomycetales, and Streptophyta in lower productivity areas. Machine learning using a random forest method successfully predicted productivity based on the microbiome composition with the best accuracy of 0.79 at the order level. Our study showed that crop productivity differences were associated with bulk soil microbiome composition and highlighted several nitrogen utility-related taxa. We demonstrated the merit of MWAS and machine learning for the first time in a plant-microbiome study.

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Physiological and management factors contributing to soybean potential yield

Cited by 68 publications

References 131 publications

Selection of inbred soybean progeny (Glycine max): an approach with population effect

Selection of inbred soybean progeny (Glycine max): an approach with population effect

Is the yield change due to warming affected by photoperiod sensitivity? Effects of the soybean E4 locus

Metagenome-Wide Association Study and Machine Learning Prediction of Bulk Soil Microbiome and Crop Productivity

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