Genetic Improvement of U.S. Soybean in Maturity Groups II, III, and IV

Rincker, Keith; Nelson, Randall L.; Specht, James E.; Sleper, D. A.; Cary, T. R.; Cianzio, Silvia R.; Casteel, Shaun N.; Conley, Shawn P.; Chen, Pengyin; Davis, Vince M.; Fox, Carolyn M.; Graef, George L.; Godsey, Chad B.; Holshouser, David Lee; Jiang, Guang‐Zhen; Kantartzi, Stella K.; Kenworthy, W. J.; Lee, Chad; Mian, Rouf; McHale, Leah K.; Naeve, Seth L.; Orf, J. H.; Poysa, Vaino; Schapaugh, William T.; Shannon, Grover; Uniatowski, Robert; Wang, Dechun; Diers, Brian W.

doi:10.2135/cropsci2013.10.0665

Cited by 173 publications

(237 citation statements)

References 41 publications

(123 reference statements)

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“…Similar increases in grain sorghum, corn and soybean production were observed in the study region during the period as a result of hybrid improvement and adoption of better agronomic practices Assefa and Staggenborg 2011;Assefa et al 2012;Rincker et al 2014). Agricultural changes are further influenced by policy changes, urban development, and economic and social changes (Bryant et al 2000).…”

Section: Validation Of Eisupporting

confidence: 59%

A system’s approach to assess the exposure of agricultural production to climate change and variability

2016

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Estimating the exposure of agriculture to climate variability and change can help us understand key vulnerabilities and improve adaptive capacity, which is vital to secure and increase world food production to feed its growing population. A number of indices to estimate exposure are available in literature. However, testing or validating them is difficult and reveals a considerable variability, and no systematic methodology has been developed to guide users in selecting indices for particular applications. This need is addressed in this paper by developing a flowchart from a conceptual model that uses a system's approach. Also, we compare five approaches to estimate exposure indices (EIs) to study the exposure of agriculture to climate variability and change: single stressor-mean climate, single stressor-extreme climate, multiple stressor-mean climate, multiple stressor-extreme climate; and combinations of the above approaches. The developed flowchart requires gathering information on the region of study, including its agriculture, stressor(s), climate factor(s) (CF), period of interest and the method of aggregation. The flowchart was applied to a case study in Kansas to better understand the five approaches to estimate EIs and the implications of the choices made in each step on the estimated the exposure. The flowchart provides options that guide EI estimation by selecting the most appropriate stressor(s), associated CF(s), and aggregation methods when a detailed methodological analysis is possible, or proposes a default method when data or resources do not allow a detailed analysis. Climate adaptation involves integration of a multitude of factors across complex systems. A more standardized approach to Climatic Change (2016) 136:647-659

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Section: Validation Of Eisupporting

confidence: 59%

A system’s approach to assess the exposure of agricultural production to climate change and variability

2016

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“…Multiple reports point to both increased total DM and a greater HI for new genetics within current production systems (Sadler and Karlen, 1995;Sinclair, 1998;Morrison et al, 1999;Kumudini et al, 2001;Koester et al, 2014;Rincker et al, 2014). Koester et al (2014) found that HI increased by 0.0022% each year between 1923 and 2007 for commercially available varieties.…”

Section: Dry Matter and Nitrogen Uptake Partitioning And Removal Acmentioning

confidence: 99%

Dry Matter and Nitrogen Uptake, Partitioning, and Removal across a Wide Range of Soybean Seed Yield Levels

Gaspar

Laboski²,

Naeve

et al. 2017

Crop Science

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Soybean [Glycine max (L.) Merr.] growers are concerned that soybean yield is restricted by limitations on biological N2 fixation and soil nitrogen (N) mineralization. However, a comprehensive study characterizing actual soybean N requirements across wide‐ranging seed yield environments is nonexistent for modern soybean production systems. Using six site‐years and eight soybean varieties, plants were sampled at six growth stages and partitioned into their respective plant parts and analyzed. For each kilogram increase in yield, total dry matter accumulation, harvest index, and total N uptake increased by 1.45 kg, 0.0034%, and 0.054 kg, respectively, but all varied by environment at any specific yield level, whereas N removal did not (0.055 kg N kg−1 grain). Nitrogen harvest index (NHI) increased (0.0019–0.004% kg−1 grain) with yield but varied by environment and yield level, resulting in indices between 73 and 90%. Peak uptake rates for N were 3.6 to 4.3 kg ha−1 d−1 between R4 and R5, depending on the yield level. After R5.5, 66 to 69% of vegetative N was remobilized to the seed, which accounted for 50.4% of seed N at the low yield level (3608 kg ha−1), but only 38.9% at the high yield level (5483 kg ha−1). Moreover, higher yields attained a greater portion of their total N uptake after R5.5 (40.1%) compared with the low yield level (29.7%). These results highlight greater remobilization efficiencies and late‐season N uptake in conjunction with greater NHI to support higher yields per unit of N uptake in current production realities.

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“…Breeding for greater yields in optimal conditions has resulted in greater sensitivity to the environment (i.e. greater yield instability in less favorable conditions) in maize (Lobell et al, 2014) and soybean (Koester et al, 2014;Rincker et al, 2014). This may explain the lack of significant correlations between « c and density in maize.…”

Section: Trends In Major Food Crop « C Over the Past Few Decadesmentioning

confidence: 99%

Photosynthetic Energy Conversion Efficiency: Setting a Baseline for Gauging Future Improvements in Important Food and Biofuel Crops

Slattery

Ort

2015

Plant Physiol.

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ORCID IDs: 0000-0002-7165-906X (R.A.S.); 0000-0002-5435-4387 (D.R.O.).The conversion efficiency (« c ) of absorbed radiation into biomass (MJ of dry matter per MJ of absorbed photosynthetically active radiation) is a component of yield potential that has been estimated at less than half the theoretical maximum. Various strategies have been proposed to improve « c , but a statistical analysis to establish baseline « c levels across different crop functional types is lacking. Data from 164 published « c studies conducted in relatively unstressed growth conditions were used to determine the means, greatest contributors to variation, and genetic trends in « c across important food and biofuel crop species. « c was greatest in biofuel crops (0.049-0.066), followed by C 4 food crops (0.046-0.049), C 3 nonlegumes (0.036-0.041), and finally C 3 legumes (0.028-0.035). Despite confining our analysis to relatively unstressed growth conditions, total incident solar radiation and average growing season temperature most often accounted for the largest portion of « c variability. Genetic improvements in « c , when present, were less than 0.7% per year, revealing the unrealized potential of improving « c as a promising contributing strategy to meet projected future agricultural demand.

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Genetic Improvement of U.S. Soybean in Maturity Groups II, III, and IV

Cited by 173 publications

References 41 publications

A system’s approach to assess the exposure of agricultural production to climate change and variability

A system’s approach to assess the exposure of agricultural production to climate change and variability

Dry Matter and Nitrogen Uptake, Partitioning, and Removal across a Wide Range of Soybean Seed Yield Levels

Photosynthetic Energy Conversion Efficiency: Setting a Baseline for Gauging Future Improvements in Important Food and Biofuel Crops

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