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Key message Software for high imputation accuracy in soybean was identified. Imputed dataset could significantly reduce the interval of genomic regions controlling traits, thus greatly improve the efficiency of candidate gene identification. Abstract Genotype imputation is a strategy to increase marker density of existing datasets without additional genotyping. We compared imputation performance of software BEAGLE 5.0, IMPUTE 5 and AlphaPlantImpute and tested software parameters that may help to improve imputation accuracy in soybean populations. Several factors including marker density, extent of linkage disequilibrium (LD), minor allele frequency (MAF), etc., were examined for their effects on imputation accuracy across different software. Our results showed that AlphaPlantImpute had a higher imputation accuracy than BEAGLE 5.0 or IMPUTE 5 tested in each soybean family, especially if the study progeny were genotyped with an extremely low number of markers. LD extent, MAF and reference panel size were positively correlated with imputation accuracy, a minimum number of 50 markers per chromosome and MAF of SNPs > 0.2 in soybean line were required to avoid a significant loss of imputation accuracy. Using the software, we imputed 5176 soybean lines in the soybean nested mapping population (NAM) with high-density markers of the 40 parents. The dataset containing 423,419 markers for 5176 lines and 40 parents was deposited at the Soybase. The imputed NAM dataset was further examined for the improvement of mapping quantitative trait loci (QTL) controlling soybean seed protein content. Most of the QTL identified were at identical or at similar position based on initial and imputed datasets; however, QTL intervals were greatly narrowed. The resulting genotypic dataset of NAM population will facilitate QTL mapping of traits and downstream applications. The information will also help to improve genotyping imputation accuracy in self-pollinated crops.

Soybean [Glycine max (L.) Merr.] is a unique crop species because it has high levels of both protein and oil in its seed. Of the many quantitative trait loci (QTL) controlling soybean seed protein content, alleles of the cqSeed protein-003 QTL on chromosome 20 exert the greatest additive effect. The high-protein allele exists in both cultivated and wild soybean (Glycine soja SieboldZucc.) germplasm. Our objective was to fine map this QTL to enable positional-based cloning of its underlying causative gene(s). Fine mapping was achieved by developing and testing a series of populations in which the chromosomal region surrounding the segregating high- versus low-protein alleles was gradually narrowed, using marker-based detection of recombinant events. The resultant 77.8 kb interval was directly sequenced from a G. soja source and compared with the reference genome to identify structural and sequence polymorphisms. An insertion/deletion variant detected in Glyma.20G85100 was found to have near-perfect +/− concordance with high/low-protein allele genotypes inferred for this QTL in parents of published mapping populations. The indel structure was concordant with an evolutionarily recent insertion of a TIR transposon into the gene in the low-protein lineage. Seed protein was significantly greater in soybean expressing an RNAi hairpin downregulation element in two independent events relative to control null segregant lineages. We conclude that a transposon insertion within the CCT domain protein encoded by the Glyma.20G85100 gene accounts for the high/low seed protein alleles of the cqSeed protein-003 QTL.

Ontogenic changes in soybean radiation-use efficiency (RUE) have been attributed to variation in specific leaf nitrogen (SLN) based only on data collected during seed filling. We evaluated this hypothesis using data on leaf area, absorbed radiation (ARAD), aboveground dry matter (ADM), and plant nitrogen (N) concentration collected during the entire crop season from seven field experiments conducted in a stress-free environment. Each experiment included a full N treatment that received ample N fertilizer and a zero N treatment that relied on N fixation and soil N mineralization. We estimated RUE based on changes in ADM between sampling times and associated ARAD, accounting for changes in biomass composition. The RUE and SLN exhibited different seasonal patterns: a bell-shaped pattern with a peak around the beginning of seed filling, and a convex pattern followed by an abrupt decline during late seed filling, respectively. Changes in SLN explained the decline in RUE during seed filling but failed to predict changes in RUE in earlier stages and underestimated the maximum RUE observed during pod setting. Comparison between observed and simulated RUE using a process-based crop simulation model revealed similar discrepancies. The decoupling between RUE and SLN during early crop stages suggests that leaf N is above that needed to maximize crop growth but may play a role in storing N that can be used in later reproductive stages to meet the large seed N demand associated with high-yielding crops.

Photosynthesis is a key target to improve crop production in many species including soybean [Glycine max (L.) Merr.]. A challenge is that phenotyping photosynthetic traits by traditional approaches is slow and destructive. There is proof-of-concept for leaf hyperspectral reflectance as a rapid method to model photosynthetic traits. However, the crucial step of demonstrating that hyperspectral approaches can be used to advance understanding of the genetic architecture of photosynthetic traits is untested. To address this challenge, we used full-range (500–2,400 nm) leaf reflectance spectroscopy to build partial least squares regression models to estimate leaf traits, including the rate-limiting processes of photosynthesis, maximum Rubisco carboxylation rate, and maximum electron transport. In total, 11 models were produced from a diverse population of soybean sampled over multiple field seasons to estimate photosynthetic parameters, chlorophyll content, leaf carbon and leaf nitrogen percentage, and specific leaf area (with R2 from 0.56 to 0.96 and root mean square error approximately

Ontogenic changes in soybean radiation-use efficiency (RUE) have been attributed to variation in specific leaf nitrogen (SLN) based only on data collected during seed filling. We evaluated this hypothesis using data on leaf area, absorbed photosynthetically active radiation (APAR), aboveground dry matter (ADM), and plant nitrogen (N) concentration collected during the entire crop season from seven field experiments conducted in a stress-free environment. Each experiment included a full N treatment that received ample N fertilizer and a zero N treatment that relied on N fixation and soil N mineralization. We estimated RUE based on changes in ADM between sampling times and associated APAR, accounting for changes in biomass composition. The SLN and RUE exhibited different seasonal patterns: a bell-shaped pattern with a peak around the beginning of seed filling, and a convex pattern followed by an abrupt decline during late seed filling, respectively. The level of N supply influenced SLN more than RUE via changes in leaf N concentration, with small changes in specific leaf weight. Changes in SLN explained the decline in RUE during seed filling but failed to predict changes in RUE in earlier stages. A simple approach based on phenological stages may give more realistic estimates of RUE before seed filling, improving crop growth and yield prediction via crop models and remote sensing.HighlightChanges in radiation-use efficiency during soybean vegetative and early reproductive stages are not related to specific leaf nitrogen.

Recent reviews about soybean nitrogen (N) requirement have speculated about a possible N limitation in high-yield environments (>4.5 Mg ha−1). However, these studies did not provide definitive experimental data to test that hypothesis and results from the literature are conflicting. To fill this knowledge gap, we evaluated N limitation across 13 high-yield soybean environments in Argentina and USA. Each experiment included a ‘zero-N’ treatment, which forced the crop to rely on biological N2 fixation and indigenous soil N, and a ‘full-N’ treatment, which provided an ample fertilizer N supply during the entire crop cycle based on site-specific yield potential. Accumulated N in aboveground dry matter (ADM) measured in a N-omission maize plot grown adjacent to the soybean experiments was used to determine indigenous soil N supply. Soybean seed yield, protein and oil concentration, ADM, harvest index (HI), accumulated N in ADM, N harvest index (NHI), and seed N were measured in both treatments at physiological maturity. A simple conceptual framework relating ADM with accumulated N was used to assess treatment differences in ADM, accumulated N, and N-use efficiency (NUE). To account for treatment differences in seed biomass composition, mass-based ADM was expressed in glucose equivalents (ADMe). Seed yield ranged from 4.6 to 6.7 Mg ha−1 (full-N) and 4 to 5.8 Mg ha−1 (zero-N), with the full-N treatment averaging 12% (0.6 Mg ha−1) and 3% (9 g kg−1) higher seed yield and protein concentration, respectively. The full-N treatment exhibited 18% (70 kg N ha−1) and 14% (1.6 Mg ha−1) greater accumulated N and ADM, respectively, compared with the zero N treatment, without changes in HI and NHI, but slightly lower NUE (29 versus 30 kg ADM kg−1 N in full and zero-N, respectively). However, NUE differences between treatments became indistinguishable when ADM was expressed as ADMe (45 kg glucose kg−1 N). The (full-N minus zero-N) seed yield difference and the indigenous soil N supply were negatively associated. This research documented (i) the existence of N limitation in high-yield soybean, (ii) that seed yield and protein concentration increases were physiologically associated with changes in accumulated N (but not in NUE and/or NHI), and (iii) that the degree of N limitation is modulated by indigenous soil N supply. Findings from this study can serve as a basis to identify environments with the largest N limitation and thus guide N management in soybean.

Global crop demand is expected to increase by 60–110% by 2050. Climate change has already affected crop yields in some countries, and these effects are expected to continue. Identification of weather-related yield-limiting conditions and development of strategies for agricultural adaptation to climate change is essential to mitigate food security concerns. Here we used machine learning on US soybean yield data, collected from cultivar trials conducted in 27 states from 2007 to 2016, to examine crop sensitivity to varying in-season weather conditions. We identified the month-specific negative effect of drought via increased water vapor pressure deficit. Excluding Texas and Mississippi, where later sowing increased yield, sowing 12 days earlier than what was practiced during this decade across the US would have resulted in 10% greater total yield and a cumulative monetary gain of ca. US$9 billion. Our data show the substantial nation- and region-specific yield and monetary effects of adjusting sowing timing and highlight the importance of continuously quantifying and adapting to climate change. The magnitude of impact estimated in our study suggest that policy makers (e.g., federal crop insurance) and laggards (farmers that are slow to adopt) that fail to acknowledge and adapt to climate change will impact the national food security and economy of the US.

Narrowing row width in soybean fields leads to earlier canopy closure, which may increase capture of incoming solar radiation during critical crop stages for yield determination. Theoretically, this should enhance seed yield. However, in prior studies, the impact of narrowing row spacing on soybean yield has been inconsistent. To explore on a broader scale the potential factors underlying this inconsistency, we evaluated the yield difference between narrow (NR; ≈38 cm) and wide (WR; ≈76 cm) row spacing using two sources of yield and management information: (i) data collected from 4879 soybean production fields via a multi-year, multi-state survey of soybean producers in the North Central US region; and (ii) data extracted from 129 site-year experiments that quantified NR-WR yield difference. The producer fields were allocated to their respective climate-soil domains to enable analysis of the NR-WR yield difference within each domain. The experimental trial data originated from three US geographic regions: south, central, and north. Key crop developmental stages in each trial were estimated using a soybean crop simulation model to discern if changes in crop phenology or any weather variable occurring before versus after a specific crop stage modulated the magnitude of the NR-WR yield difference. Analysis of experimental trial data indicated that, while NR yields were overall higher than WR yields, the NR-WR yield difference varied by region: 540 (south), 104 (central), and 240 kg ha−1 (north); the respective NR yields were greater than WR yields in 92%, 68%, and 84% of the cases. In the north and south regions, the NR-WR yield difference increased when the crop cycle length decreased as a consequence of later sowing date, earlier cultivar maturity group, and/or higher temperature. The relatively smaller (and occasionally negative) NR-WR yield difference detected in the central region was likely the result of environmental conditions that favored canopy closure irrespective of row spacing. In contrast to the analysis of the experimental database, no consistent NR-WR yield differences were detected in the producer field database. We hypothesize that the apparent absence of a significant NR-WR effect in the producer dataset is likely associated with the background management used with narrow spacing, together with yield losses due to wheel damage and greater disease pressure. This complementary approach using both producer and experimental data can help evaluate if practices documented in experimental trials to enhance yield realize equivalent yield increases in producer fields and, if not, explore underlying causes for the discrepancy.

Soybean is the world’s leading source of vegetable protein and demand for its seed continues to grow. Breeders have successfully increased soybean yield, but the genetic architecture of yield and key agronomic traits is poorly understood. We developed a 40-mating soybean nested association mapping (NAM) population of 5,600 inbred lines that were characterized by single nucleotide polymorphism (SNP) markers and six agronomic traits in field trials in 22 environments. Analysis of the yield, agronomic, and SNP data revealed 23 significant marker-trait associations for yield, 19 for maturity, 15 for plant height, 17 for plant lodging, and 29 for seed mass. A higher frequency of estimated positive yield alleles was evident from elite founder parents than from exotic founders, although unique desirable alleles from the exotic group were identified, demonstrating the value of expanding the genetic base of US soybean breeding.

Identification of causes of gaps between yield potential and producer yields has been restricted to small geographic areas. In the present study, we developed a novel approach for identifying causes of yield gaps over large agricultural areas with diversity in climate and soils. This approach was applied to quantify and explain yield gaps in rainfed and irrigated soybean in the North-Central USA (NC USA) region, which accounts for about one third of soybean global production. Survey data on yield and management were collected from 3568 producer fields over two crop seasons and grouped into 10 technology extrapolation domains (TEDs) according to their soil, climate, and water regime. Yield potential was estimated using a combination of crop modeling and boundary functions for water productivity and compared against highest producer yields derived from the yield distribution in each TED-year. Yield gaps were calculated as the difference between yield potential and average producer yield. Explanatory factors for yield gaps were investigated by identifying management practices that were concordantly associated with high- and low-yield fields. Management × TED interactions were then evaluated to elucidate the underlying causes of yield gaps. The chosen spatial TED framework accounted for about half of the regional variation in producer yield within the NC USA region. Across the 10 TEDs, soybean average yield potential ranged from 3.3 to 5.3 Mg ha −1 for rainfed fields and from 5.3 to 5.6 Mg ha −1 for irrigated fields. Highest producer yields in each TED were similar (±12%) to the estimated yield potential. Yield gap, calculated as percentage of yield potential, was larger in rainfed (range: 15–28%) than in irrigated (range: 11–16%) soybean. Upscaled to the NC USA region, yield potential was 4.8 Mg ha −1 (rainfed) and 5.7 Mg ha −1 (irrigated), with a respective yield gap of 22 and 13% of yield potential. Sowing date, tillage, and in-season foliar fungicide and/or insecticide were identified as explanatory causes for yield variation in half or more of the 10 TEDs. However, the degree to which these three factors influenced producer yield varied across TEDs. Analysis of in-season weather helped interpret management × TED interactions. For example, yield increase due to advances in sowing date was greater in TEDs with less water limitation during the pod-setting phase. The present study highlights the strength of combining producer survey data with a spatial framework to measure yield gaps, identify management factors explaining these gaps, and understand the biophysical drivers influencing yield responses to crop management.

As soybean yield continues to increase, it seems critical to know if there is a yield level at which potential contribution of indigenous nitrogen (N) sources (N fixation and soil mineralization) becomes insufficient to meet crop N requirements for high yields, while still maintaining or increasing protein and oil concentration. We have hypothesized that, in absence of other limiting factors, degree of N limitation increases with increasing yield potential (Yp) of the production environment. To test this hypothesis, we developed a novel protocol to ensure an ample N supply during the entire crop season (full-N treatment). That protocol was applied to field-grown irrigated soybean in Balcarce (Argentina) and Nebraska (USA), where measured full-N seed yields were ±15% of their simulated Yp in 92% of the cases. The combination of locations, years, sowing dates, and N treatments resulted in a wide range of seed yields, from 2.5 to 6.5 Mg ha−1. Overall, full-N seed yield averaged 11% higher than seed yield without N addition (zero-N). However, magnitude of yield difference between full-N and zero-N depended upon Yp, ranging from no detectable yield difference in low-Yp (ca. 2.5 Mg ha−1) to up to 900 kg ha−1 in high-Yp environments (ca. 6 Mg ha−1). Seed yield differences were associated with higher aboveground dry matter, seed number, and seed weight in the full-N versus zero-N treatments. Seed protein (but not oil) concentration was higher in the full-N treatment, and both protein and oil yields were higher in the full-N versus zero-N treatments. Findings from this study indicate that (i) N limits soybean seed yield (as well as protein yield, and oil yield) in environments with high Yp, where indigenous N sources seem insufficient to fully satisfy crop N requirements, and (ii) yield response to N fertilizer can occur above a 2.5 Mg ha−1 Yp threshold and has an upper limit of 250 kg seed per Mg increase in Yp. Fil: Cafaro la Menza, Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. University of Nebraska; Estados Unidos Fil: Monzon, Juan Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Sur. Estación Experimental Agropecuaria Balcarce; Argentina Fil: Specht, James E.. University of Nebraska; Estados Unidos Fil: Grassini, Patricio. University of Nebraska; Estados Unidos

Soybean ( Glycine max ) production is expanding into cooler and warmer environments. Temperature influence on nodal leaf appearance rate and, especially, nodal primordium formation rate – two critical parameters that determine potential leaf area, light absorption, and crop growth and yield – is poorly understood. This study was designed to determine the influence of temperature on primordium formation and leaf appearance and to investigate how the co-ordination between these two processes is affected by contrasting temperature regimes. The experiments were conducted in field and greenhouse settings using indeterminate cultivars differing in maturity group. Soil and air temperature was measured at 30-min intervals in all experiments, starting at sowing and ending at physiological maturity. Plants were dissected every 4–7 d to determine the number of primordia at the stem apical meristem and to estimate primordium formation rate. The number of nodal leaves at main stem was assessed periodically during the entire growing season to estimate rate of leaf appearance. Primordium formation ended near the beginning of pod setting, while leaf appearance ceased at the beginning of seed filling. The end-season final number of primordia was greater than the final leaf number, revealing a surplus of primordia that did not advance beyond primordial stage. Across experiments, mean temperature during the phases of primordium formation and leaf appearance ranged from 15 to 26 °C. Both primordium formation and leaf appearance were temperature-dependent, but primordium formation was faster than leaf appearance. The plastochron was 36 °Cd, whereas the phyllochron was biphasic, decreasing from 83 °Cd to 58 °Cd during ontogeny. A strong relationship was found between the number of primordia and the number of leaves, which was stable across experiments, temperature treatments, and cultivars. This study has established that both primordium formation and leaf appearance in soybean are influenced by temperature, with a co-ordination between these two processes. The co-ordination model presented here can be used for robust prediction of seasonal nodal leaf dynamics in soybean.

The identification and mobilization of useful genetic variation from germplasm banks for use in breeding programs is critical for future genetic gain and protection against crop pests. Plummeting costs of next-generation sequencing and genotyping is revolutionizing the way in which researchers and breeders interface with plant germplasm collections. An example of this is the high density genotyping of the entire USDA Soybean Germplasm Collection. We assessed the usefulness of 50K single nucleotide polymorphism data collected on 18,480 domesticated soybean (Glycine max) accessions and vast historical phenotypic data for developing genomic prediction models for protein, oil, and yield. Resulting genomic prediction models explained an appreciable amount of the variation in accession performance in independent validation trials, with correlations between predicted and observed reaching up to 0.92 for oil and protein and 0.79 for yield. The optimization of training set design was explored using a series of cross-validation schemes. It was found that the target population and environment need to be well represented in the training set. Second, genomic prediction training sets appear to be robust to the presence of data from diverse geographical locations and genetic clusters. This finding, however, depends on the influence of shattering and lodging, and may be specific to soybean with its presence of maturity groups. The distribution of 7608 nonphenotyped accessions was examined through the application of genomic prediction models. The distribution of predictions of phenotyped accessions was representative of the distribution of predictions for nonphenotyped accessions, with no nonphenotyped accessions being predicted to fall far outside the range of predictions of phenotyped accessions.

Seed number (SN) is positively associated with aboveground dry matter (ADM) accumulation in most crop species. In soybean, there are discrepancies among studies relative to the start and end of the critical period for seed number determination. The objective of this study was to more precisely identify those two stages. To do so, we followed two approaches: (i) analyses of experimental data where differences in SN occurred as a result of variation in crop growth due to weather and management practices and (ii) review of published experiments where soybean was subjected to artificial stresses during specific crop phases. Following the first approach, we analyzed the relationships between SN versus ADM accumulation and its components, crop growth rate (CGR) and duration, for different reproductive phases using data from experiments that portrayed a wide range of environments and management practices. In the second approach, we compared SN values in stressed versus non-stressed treatments. Accumulated ADM during the R3 to R6 phase explained ca. 70 % of the variation in SN across experiments. The CGR during R3-R6 was also a good predictor of SN, but it had lower explanatory power compared with accumulated ADM, because CGR did not account for changes in duration of critical period across experiments. Review of published data, wherein soybean crops were subjected to artificial stresses during different reproductive phases, confirmed that the R3-R6 phase is a critical phase for SN determination in soybean. Key findings of this study were: (i) critical period for SN determination in soybean occurs during the R3-R6 phase and (ii) accumulated ADM during the critical period is a better predictor of SN compared with CGR because it accounts for differences in the duration of the critical period as a result of weather and management.

Producer field-level data have been used in recent studies to identify suites of management practices that consistently increase yield for a given climate-soil environment. However, the physiological drivers underlying the empirical associations between yield and management practices derived from these studies have remained mostly speculative, particularly in terms of resource capture and conversion into economic yield. We followed an approach consisting of a large producer database, satellite imagery, and crop modeling to assess which key physiological parameters best explain on-farm yield response to producer-chosen management practices across different climate-soil production environments. Survey data on yield and management practices were collected from 5291 soybean fields sown across the US North Central region during 2014-2016. Producer data were grouped into 10 technology extrapolation domains (TEDs) to account for variation in soil, climate, and water regime across producer fields. Simulated phenology and satellite imagery were used to estimate incident (IPAR) and absorbed (APAR) photosynthetically active radiation during the entire crop season and also during the time-span beginning with pod setting and ending with physiological maturity. In each TED, seed yield was increased by early sowing (+13 to +39 kg ha-1d-1), which was physiologically driven by increases in the duration of both crop cycle and critical period, and by a higher APAR. In-season application of foliar fungicide and/or insecticide also resulted in significant yield increase (+0.35 Mg ha−1, p

Although the U.S. accounts for 38% of global soybean production, and produces 85 Mt annually, quantification of U.S. soybean yield gaps and assessment of underpinning causes have not been performed. Likewise, no attempt has been made to estimate the attainable water productivity (kg seed per mm of water supply). Here we make an initial attempt at such analyses by evaluating yield gaps and water productivity of soybean in the state of Nebraska (western U.S. Corn Belt), which ranks fifth among U.S. soybean producing states with a 6.5 Mt annual production. Rainfed and irrigated production are both important in Nebraska, accounting for 46% and 54% of total state soybean output. We evaluated a database containing on-farm field yields, applied inputs, and management practices collected from 516 soybean fields in the eastern and central regions of Nebraska during three successive crop seasons (2010–2012). Yield gaps were estimated as the difference between yield potential and on-farm field yields. Yield potential was estimated by two approaches: (i) a soybean simulation model coupled with field-specific weather and management data, and (ii) derivation of a boundary function for the relationship between producer-reported soybean yields and seasonal water supply using quantile regression. Actual yields were evaluated in relation to applied inputs and management factors to identify the most likely causes of yield gaps. Across regions, average yield gap of irrigated soybean ranged from 10 to 30% of the simulated yield potential. Water supply set an upper limit to productivity, not only in rainfed fields, but also in irrigated fields in a drought year in which irrigation did not fully satisfy crop water requirements. The boundary function for the relationship between yield and seasonal water supply had a slope (≈attainable water productivity) of 9.9 kg ha−1 mm−1 and x-intercept (≈soil evaporation) of 73 mm. A seasonal water supply of ca. 650 mm appeared sufficient to maximize seed yield. Average rainfed and irrigated yields were 31 and 20% below their respective yield potential estimated from the boundary function. Sowing date also set an upper limit to producer yield, and this management practice alone explained the largest portion of the observed yield variation among fields. Soybean yield linearly decreased with sowing date delay, with the magnitude of the yield penalty varying among regions. Irrigated yields were not higher in no-till fields, and, in fact, a yield penalty was observed in no-till fields compared to reduced- or disked fields, especially in region-years with cooler early-season temperatures. Higher irrigated and rainfed yields were observed in fields that received starter fertilizer or P fertilizer application. Finally, application of in-season fungicide enhanced irrigated crop yields though this effect was inconsistent over years. Analysis of this database indicated that (i) soybean producers in this region obtained yields close (70–90%) to the estimated yield potential ceiling, and (ii) future agronomic on-farm yield improvement might be achieved by fine-tuning of current management practices, including earlier sowing date coupled with judiciously chosen tillage to achieve warmer soils in the springs plus suitably applied nutrient fertilizer application and in-season fungicide.

Genetic improvement toward optimized and stable agronomic performance of soybean genotypes is desirable for food security. Understanding how genotypes perform in different environmental conditions helps breeders develop sustainable cultivars adapted to target regions. Complex traits of importance are known to be controlled by a large number of genomic regions with small effects whose magnitude and direction are modulated by environmental factors. Knowledge of the constraints and undesirable effects resulting from genotype by environmental interactions is a key objective in improving selection procedures in soybean breeding programs. In this study, the genetic basis of soybean grain yield responsiveness to environmental factors was examined in a large soybean nested association population. For this, a genome-wide association to performance stability estimates generated from a Finlay-Wilkinson analysis and the inclusion of the interaction between marker genotypes and environmental factors was implemented. Genomic footprints were investigated by analysis and meta-analysis using a recently published multiparent model. Results indicated that specific soybean genomic regions were associated with stability, and that multiplicative interactions were present between environments and genetic background. Seven genomic regions in six chromosomes were identified as being associated with genotype-by-environment interactions. This study provides insight into genomic assisted breeding aimed at achieving a more stable agronomic performance of soybean, and documented opportunities to exploit genomic regions that were specifically associated with interactions involving environments and subpopulations.

Soybean (Glycine max) is the most widely grown oilseed in the world and is an important source of protein for both humans and livestock. Soybean is widely adapted to both temperate and tropical regions, but a changing climate demands a better understanding of adaptation to specific environmental conditions. Here, we explore genetic variation in a collection of 3,012 georeferenced, locally adapted landraces from a broad geographical range to help elucidate the genetic basis of local adaptation. We used geographic origin, environmental data and dense genome-wide SNP data to perform an environmental association analysis and discover loci displaying steep gradients in allele frequency across geographical distance and between landrace and modern cultivars. Our combined application of methods in environmental association mapping and detection of selection targets provide a better understanding of how geography and selection may have shaped genetic variation among soybean landraces. Moreover, we identified several important candidate genes related to drought and heat stress, and revealed important genomic regions possibly involved in the geographic divergence of soybean.

A set of nested association mapping (NAM) families was developed by crossing 40 diverse soybean [ (L.) Merr.] genotypes to the common cultivar. The 41 parents were deeply sequenced for SNP discovery. Based on the polymorphism of the single-nucleotide polymorphisms (SNPs) and other selection criteria, a set of SNPs was selected to be included in the SoyNAM6K BeadChip for genotyping the parents and 5600 RILs from the 40 families. Analysis of the SNP profiles of the RILs showed a low average recombination rate. We constructed genetic linkage maps for each family and a composite linkage map based on recombinant inbred lines (RILs) across the families and identified and annotated 525,772 high confidence SNPs that were used to impute the SNP alleles in the RILs. The segregation distortion in most families significantly favored the alleles from the female parent, and there was no significant difference of residual heterozygosity in the euchromatic vs. heterochromatic regions. The genotypic datasets for the RILs and parents are publicly available and are anticipated to be useful to map quantitative trait loci (QTL) controlling important traits in soybean.

Genome-wide association (GWA) has been used as a tool for dissecting the genetic architecture of quantitatively inherited traits. We demonstrate here that GWA can also be highly useful for detecting many major genes governing categorically defined phenotype variants that exist for qualitatively inherited traits in a germplasm collection. Genome-wide association mapping was applied to categorical phenotypic data available for 10 descriptive traits in a collection of ∼13,000 soybean [ (L.) Merr.] accessions that had been genotyped with a 50,000 single nucleotide polymorphism (SNP) chip. A GWA on a panel of accessions of this magnitude can offer substantial statistical power and mapping resolution, and we found that GWA mapping resulted in the identification of strong SNP signals for 24 classical genes as well as several heretofore unknown genes controlling the phenotypic variants in those traits. Because some of these genes had been cloned, we were able to show that the narrow GWA mapping SNP signal regions that we detected for the phenotypic variants had chromosomal bp spans that, with just one exception, overlapped the bp region of the cloned genes, despite local variation in SNP number and nonuniform SNP distribution in the chip set.

Evaluation of seed protein alleles in soybean populations showed that an increase in protein concentration is generally associated with a decrease in oil concentration and yield. Soybean [Glycine max (L.) Merrill] meal is one of the most important plant-based protein sources in the world. Developing cultivars high in seed protein concentration and seed yield is a difficult task because the traits have an inverse relationship. Over two decades ago, a protein quantitative trait loci (QTL) was mapped on chromosome (chr) 20, and this QTL has been mapped to the same position in several studies and given the confirmed QTL designation cqSeed protein-003. In addition, the wp allele on chr 2, which confers pink flower color, has also been associated with increased protein concentration. The objective of our study was to evaluate the effect of cqSeed protein-003 and the wp locus on seed composition and agronomic traits in elite soybean backgrounds adapted to the Midwestern USA. Segregating populations of isogenic lines were developed to test the wp allele and the chr 20 high protein QTL alleles from Danbaekkong (PI619083) and Glycine soja PI468916 at cqSeed protein-003. An increase in protein concentration and decrease in yield were generally coupled with the high protein alleles at cqSeed protein-003 across populations, whereas the effects of wp on protein concentration and yield were variable. These results not only demonstrate the difficulty in developing cultivars with increased protein and yield but also provide information for breeding programs seeking to improve seed composition and agronomic traits simultaneously.

Yield depends upon the amount of photosynthetically active radiation (PAR) absorbed by the crop during the growing season and its conversion into harvestable biomass. Little is known about attainable and actual efficiencies involved in absorbing incident PAR (ea) and converting it into yield (ec) in producer fields. We developed a novel approach consisting of producer data, satellite imagery, and crop modelling to assess ea and ec in agro-ecosystems. Simulated phenology and satellite imagery were used to estimate incident (IPAR) and absorbed (APAR) PAR during the crop season for 3096 soybean fields sown across the US North Central region during 2014–2016. Quantile regression was used to derive upper limits for ea and ec and multiple-regression analysis was performed to identify biophysical drivers of the observed variation in these two efficiencies. Differences in weather, soil, and management resulted in a wide range in crop cycle length (58–126 d), total IPAR (580–1250 MJ m−2), and seed yield (0.8–6.3 Mg ha−1). The relationship between yield and total APAR was curvilinear, indicating marginal yield gains in the upper APAR range. Attainable ea represented 65% of total IPAR, while attainable ec ranged from 0.6 to 1.2 g seed MJ−1 depending upon total APAR. Average producer ea and ec were 14 and 29% below their attainable efficiencies. Average efficiency in converting IPAR into yield was 0.8%, with an upper limit of 1.1% derived from the 95th percentile of the field data distribution. Although weak, relationships between efficiencies and meteorological factors such as IPAR, temperature, water balance, and diffuse radiation were consistent with previous literature. There was a strong trade-off between ec and ea indicating that maximizing both efficiencies simultaneously was not possible in producer fields. Our approach can be used to determine attainable and actual efficiencies in capturing and converting radiation into yield, set realistic yield limits, and understand relationships between producer yield and management practices.

On-farm U.S. soybean [Glycine max (L.) Merr.] yields have increased at an annual rate of 23.3 kg ha–¹ yr–¹ since the 1920s. These gains have come from a variety of sources including genetic, agronomic, and environmental changes. Genetic gains arising from breeding efforts have likely contributed the most to the U.S. soybean yield increase; however, the relative contribution of each source of gain is difficult to estimate. The objectives of this study were to compare yield of soybean varieties with different year of release, understand the effects of fungicide applications on soybean seed yield, and evaluate the composition of soybean cultivars chosen to represent historically significant releases in maturity groups (MGs) II and III released during the last 85 yr. A set of 116 cultivars in these two MGs, released from 1923 to 2008, received a fungicide seed treatment followed by foliar applications at R1, R3, and R5 and were compared to non-treated controls. Seed composition changed over time with protein concentration decreasing 2.1 g kg–¹ for every g kg–¹increase in oil concentration. The significant interaction between fungicide treatment and MG III cultivar release year for plant stand revealed that such treatments were more beneficial with respect to obsolete cultivars of MG III, though this plant stand interaction did not translate into a significant yield interaction. The rate of genetic yield improvement made by breeders was not influenced by fungicide management and matched the observed rate of on-farm yield improvement that occurred during the same period.

Soybean [Glycine max (L.) Merr.] yield has increased during the past century; however, little is understood about the morphological parameters that have contributed most to yield gain. We conducted field studies to determine relationships between genetic gain of soybean yield and seeding rate. The hypothesis was newer cultivars would express higher yield than older cultivars when grown in higher plant populations. A total of 116 soybean cultivars equally representing Maturity Groups (MGs) II and III released over the last 80 yr were evaluated at high and low seeding rates in Wisconsin, Minnesota, Illinois, and Indiana. Seeding rates were 445,000 and 148,000 seeds ha–¹ resulting in 311,000 and 94,000 plants ha–¹ (high and low, respectively). Seed yield was greater for the high seeding rate vs. low seeding rate throughout all cultivars and years of release, but the difference was larger in newer cultivars. The differences observed primarily came from an increased number of pods and seeds plant–¹. However, newer cultivars grown in low seeding rates increased per plant yield linearly by 0.118 (± 0.02)x– 208.0 g plant–¹, where x = year-of-release, which was three times greater than at the high seeding rate. The greater yield trend came from seeds produced on plant branches. Therefore, newer cultivars produce more compensatory yield on plant branches under lower plant populations than older cultivars, so over the last 80 yr there has been a diminishing response to the expected yield penalty from reduced plant density.

Soybean [Glycine max (L.) Merr.] yield, irrigation water use efficiency (IWUE), crop water use efficiency (CWUE), evapotranspiration water use efficiency (ETWUE), and soil water extraction response to eleven treatments of full, limited, or delayed irrigation versus a rainfed control were investigated using a subsurface drip irrigation (SDI) system at a research site in south-central Nebraska. The SDI system laterals were 0.40 m deep in every other row middle of 0.76 m spaced plant rows. Actual evapotranspiration (ET a ) was quantified in all treatments and used to schedule irrigation events on a 100% ET a replacement basis in all but three of the eleven treatments (i.e., 75% ET a replacement was used in two, and 60% ET a replacement was used in one). The irrigation amount (I a ) applied at each event was 100% of the ET a amount, except for two 100% ET a treatments in which only 65% or 50% of the water needed to cover the treatment plot area was applied to enable a test of a partial surface area-based irrigation approach. The first irrigation event was delayed until soybean stage R3 (begin pod) in two 100% I a treatments, but thereafter they were irrigated with either 100% or 75% ET a replacement. Two 100% ET a and 100% I a treatments also were used to evaluate soybean response to nitrogen (N) application methods (i.e., a preplant method versus N injection using the SDI system). Soybean ET a varied from 452 mm for the rainfed treatment to 600 mm (30% greater) for the fully irrigated treatment (100% ET a and 100% I a ) in 2007, and from 473 to 579 mm (20% greater) for the same treatments, respectively, in 2008. Among the irrigated treatments, 100% ET a and 65% I a had the lowest 2007 ET a value (557 mm), whereas 100% ET a and 50% I a had the lowest 2008 ET a (498 mm). The 100%, 75%, and 60% ET a treatments with 100% I a had respective actual ET a values that declined linearly in 2008 (i.e., 579, 538, and 498 mm), but not in 2007. Seasonal totals for ET a versus I a exhibited a linear relationship (R 2 = 0.68 in 2007 and R 2 = 0.67 in 2008). Irrigation enhanced soybean yields from rainfed yield baselines of 4.04 ton ha -1 in 2007 and 4.82 ton ha -1 in 2008) to a maximum of 4.94 ton ha -1 attained in 2007 with the delay to R3 irrigation treatment (its yield was significantly greater, p -1 attained in 2008 with the 100% ET a and 100% I a preplant N treatment. Seed yield had a quadratic relationship with irrigation water applied and a linear relationship with ET a that was stronger in the drier year of 2007. Each 25.4 mm incremental increase in seasonal irrigation water applied increased soybean yield by 0.323 ton ha -1 (beyond the intercept) in 2007 and by 0.037 ton ha -1 in 2008. Each 25.4 mm increase in ET a generated a yield increase of 0.114 ton ha -1 (beyond the intercept) in 2007, but only 0.02 ton ha -1 in the wetter year of 2008. This research demonstrated that delaying the onset of irrigation until the R3 stage and practicing full irrigation thereafter for soybean grown on silt loam soils resulted in yields (and crop water productivity) that were similar to full-season irrigation scheduling strategies, and this result may be applicable in other regions with edaphic and climatic characteristics similar to those in south-central Nebraska.

Soybean improvement via plant breeding has been critical for the success of the crop. The objective of this study was to quantify genetic change in yield and other traits that occurred during the past 80 yr of North American soybean breeding in Maturity Groups (MGs) II, III, and IV. Historic sets of 60 MG II, 59 MG III, and 49 MG IV soybean cultivars, released from 1923 to 2008, were evaluated in field trials conducted in 17 U.S. states and one Canadian province during 2010 to 2011. Averaged over 27 MG II and MG IV and 26 MG III site-years of data, the estimated rates of yield improvement during the 80 yr were 23 kg ha yr for MGs II and III, and 20 kg ha yr for MG IV cultivars. However, a two-segment linear regression model provided a better fit to the data and indicated that the average current rate of genetic yield gain across MGs is 29 kg ha yr. Modern cultivars yielded more than old cultivars in all environments, but particularly in high-yielding environments. New cultivars in the historic sets used in this study are shorter in height, mature later, lodge less, and have seeds with less protein and greater oil concentration. Given that on-farm soybean yields in the United States are also increasing at a rate of 29 kg ha yr, it can be inferred that continual release of greater-yielding cultivars has been a substantive driver of the U.S. on-farm realized yield increases.

Gene structural variation (SV) has recently emerged as a key genetic mechanism underlying several important phenotypic traits in crop species. We screened a panel of 41 soybean (Glycine max) accessions serving as parents in a soybean nested association mapping population for deletions and duplications in more than 53,000 gene models. Array hybridization and whole genome resequencing methods were used as complementary technologies to identify SV in 1528 genes, or approximately 2.8%, of the soybean gene models. Although SV occurs throughout the genome, SV enrichment was noted in families of biotic defense response genes. Among accessions, SV was nearly eightfold less frequent for gene models that have retained paralogs since the last whole genome duplication event, compared with genes that have not retained paralogs. Increases in gene copy number, similar to that described at the Rhg1 resistance locus, account for approximately one-fourth of the genic SV events. This assessment of soybean SV occurrence presents a target list of genes potentially responsible for rapidly evolving and/or adaptive traits.

Both rainfed and irrigated soybean production are important in Nebraska (western US Corn Belt), accounting for a respective 48 and 52% of the state's soybean production of 7 Mt on a respective 55 and 45% share of the state soybean area of 1.9 Mha. To date, no assessment of factors that may account for regional and inter-annual variation in yield and irrigation amount has been performed. To accomplish that objective, we evaluated a database containing on-farm field yields and total irrigation amount used in those fields. These data have been collected annually from ca. 1000 soybean fields in six regions of Nebraska during the past eight years. Distributions of farm yield and irrigation amount were analyzed and the impact of selected weather variables and key management factors on these two variables was assessed. For irrigated soybean, attainable yields were estimated from the 95th percentile of the yield distribution, and yield gaps were then calculated as the difference between the attainable yield and average farm yield. The interquartile range for yield and irrigation amount was used as a measure of the management gap between skillfully and sub-optimally managed fields. Distribution of irrigated yield and irrigation amount were skewed, indicating that many producers achieved yields relatively close to the attainable yield, but also that irrigation in excess of the amount needed may have occurred in an important fraction of the total fields. Variation in rainfed yield was strongly related to July–August total rainfall and seasonal water deficit, but, in contrast, no single meteorological factor could consistently explain variation in irrigated yield. In fact, sowing date explained most of the observed inter-annual variation in irrigated yield in all regions. Amount of irrigation applied in each year depended on both rainfall and reference evapotranspiration. Efficiency in use of irrigation water versus rainfall to produce seed yield was remarkably similar. Across regions, attainable yield of irrigated soybean in NE averaged 4.7 Mg ha −1 , with the yield gap averaging about 16% of the attainable yield. Variation in yield and irrigation amount among fields was more important than variation among years and even regions. Further research is needed to identify the causes for this observed field-to-field variation in yield and irrigation amount within the same year and region.

Similar to Arabidopsis thaliana, the wild soybeans (Glycine soja) and many cultivars exhibit indeterminate stem growth specified by the shoot identity gene Dt1, the functional counterpart of Arabidopsis TERMINAL FLOWER1 (TFL1). Mutations in TFL1 and Dt1 both result in the shoot apical meristem (SAM) switching from vegetative to reproductive state to initiate terminal flowering and thus produce determinate stems. A second soybean gene (Dt2) regulating stem growth was identified, which, in the presence of Dt1, produces semideterminate plants with terminal racemes similar to those observed in determinate plants. Here, we report positional cloning and characterization of Dt2, a dominant MADS domain factor gene classified into the APETALA1/SQUAMOSA (AP1/SQUA) subfamily that includes floral meristem (FM) identity genes AP1, FUL, and CAL in Arabidopsis. Unlike AP1, whose expression is limited to FMs in which the expression of TFL1 is repressed, Dt2 appears to repress the expression of Dt1 in the SAMs to promote early conversion of the SAMs into reproductive inflorescences. Given that Dt2 is not the gene most closely related to AP1 and that semideterminacy is rarely seen in wild soybeans, Dt2 appears to be a recent gain-of-function mutation, which has modified the genetic pathways determining the stem growth habit in soybean.

Soybean [Glycine max (L.) Merr.] grain yield has annually increased nearly 23 kg ha-1, but the interaction of genetic advancement and improved agronomic practices has not been well quantified, including N utilization and fertilization. A field study with soybean cultivars released from 1923 to 2008 in maturity group (MG) II and MG III was conducted in multiple environments with a nonlimiting supply of fertilizer N to examine the main effects and interactions of N supply and release year on grain yield and seed quality. We hypothesized that grain yield and seed quality would be improved with the nonlimiting supply of N, especially for the modern cultivars. Supplemental N totaled 560 kg N ha-1 with 40% applied at planting and 60% applied at V5. Grain yield increased with release year in MG II (17.2 kg ha-1 yr-1). Application of N to MG II cultivars increased seed protein by 10 to 19.5 g kg-1 across all release years, but grain yield and seed oil was not affected. Grain yield gains of MG III cultivars fertilized with N was 27.4 kg ha-1 yr-1, which was 20% better than unfertilized (22.8 kg ha-1 yr-1). Application of N to MG III cultivars increased seed mass (11%) across release years with no changes in seed protein and oil. The nonlimiting supply of N increased seed protein across all release years in MG II cultivars, and the N supply from the soil and biological N fixation was insufficient to maximize grain yield in modern, MG III cultivars in the tested environments. E.W. Wilson, N.H. Weidenbenner, and S.L. Naeve, Dep. of Agronomy and Plant Genetics, Univ. of Minnesota, 411 Borlaug Hall, 1991 Upper Buford Cir., St. Paul, MN 55108; S.C. Rowntree, S.P. Conley, and V.M. Davis, Dep. of Agronomy, Univ. of Wisconsin-Madison, 1575 Linden Dr., Madison, WI 53706; S.N. Casteel, Dep. of Agronomy, Purdue Univ., 915 W. State Street, West Lafayette, IN 47907; J.J. Suhre and B.W. Diers, Dep. Crop Sciences, Univ. of Illinois, 1101 W. Peabody Dr., Urbana, IL 61801; P.D. Esker, Escuela de Agronomia, Universidad de Costa Rica, San Jose, Costa Rica; J.E. Specht, Dep. Of Agronomy, Univ. of Nebraska, Lincoln, NE 68583. Received 23 May 2013. *Corresponding author (scasteel@purdue.edu). Abbreviations: BNF, biological N fixation; ESN, Environmentally Smart Nitrogen; MG, maturity group; NASS, National Agricultural Statistics Service; nonnod, nonnodulating; OM, organic matter. Published in Crop Sci. 54:340–348 (2014). doi: 10.2135/cropsci2013.05.0339 Freely available online through the author-supported open-access option. © Crop Science Society of America | 5585 Guilford Rd., Madison, WI 53711 USA All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permission for printing and for reprinting the material contained herein has been obtained by the publisher. Published December 6, 2013

Hourly evapotranspiration (ET) crop coefficients (K c ) are needed to optimize the effectiveness and efficiency of high-frequency micro- and sprinkler irrigation practices involving the application of water multiple times a day. However, not much is known about the daily and seasonal patterns and magnitudes in hourly K c values for soybean. In addition, locally developed K c values are necessary for more robust within-season irrigation management, crop ET estimation, and water balance analyses. Hourly and daily K c functions were developed for soybean in south-central Nebraska through extensive field research. Actual crop evapotranspiration (ET a ) was measured using a Bowen ratio energy balance system. Daily crop coefficients were calculated as K c = ET a /ET ref , wherein reference (potential) evapotranspiration (ET ref ) was calculated using the Penman-Monteith equation with a fixed canopy resistance for both alfalfa-reference (ET r ) and grass-reference (ET o ) surfaces. The K c values were derived in two forms: (1) a single (normal or average K c ) K cr based on ET r , and K co based on ET o ; and (2) a basal coefficient (K cbr ) based on ET r , and K cbo based on ET o . The seasonal patterns of variation of K cr , K co , K cbr , and K cbo were examined on five different temporal base scales: days after emergence (DAE), cumulative growing degree days (GDD), leaf area index (LAI), fractional green canopy groundcover (CC), and plant phenology (V and R stages). The 2007 and 2008 growing season ET a totals were 535 and 514 mm, respectively. Extreme hourly K c values were frequently observed in the early morning and late afternoon hours when ET a was very low relative to ET r and ET o . Daily means of the 10 to 13 hourly values computed for K cr ranged from 0.25 to 1.06 in 2007 and from 0.15 to 1.02 in 2008, whereas those computed for K co ranged from 0.39 to 1.37 in 2007 and from 0.22 to 1.29 in 2008. Daily K cr and K co values calculated based on daily data ranged from 0.20 to 1.12 and from 0.27 to 1.47, respectively. Comparison of all daily means of hourly coefficients with the corresponding daily coefficients in one-to-one graphs and zero-origin based regression of the former on the latter revealed linear regression coefficients of 0.92 (2007 K cr ), 0.95 (2008 K cr ), 0.96 (2007 K co ), and 0.97 (2008 K co ), with R 2 values of 0.78 or better. On average, hourly K c values were about 4% to 8% lower that the corresponding daily values. Substantial diurnal variability was observed in K co and K cr measured during daylight hours (ranging from 0.1-0.2 to 1.5-1.6) from early morning to late afternoon (8:00 to 18:00), and the range of variability was substantially dependent on the coincident V and R stages. The relationship between K c and LAI was best represented by two regression trend lines: one representing crop development from its beginning up to the start of senescence, and the other representing crop development thereafter. A similar break in the regression trend line was observed in the relationship between basal K c and GDD. In contrast, the relationship between K c and fractional CC was not biphasic and could be modeled with one regression trend line. The FAO-56 tabulated K co values and those measured in this research were significantly different (p a and crop water requirement estimates. Because this research proved that K co and K cr values are not constant during the day from dawn to dusk, using daily average K co or K cr values would not be able to provide robust and precise determination of crop irrigation requirements for irrigation practices delivered more than once per day. The crop coefficients developed in this research as a function of several base scales should provide crop consultants, extension service personnel, agronomists, irrigation practitioners, and other irrigation and water management professionals with robust and accurate methods for choosing and applying crop coefficients to be used for more precise determination of ET a and water requirements, thus leading to more efficient and effective seasonal soybean irrigation management.

Mean on-farm U.S. soybean [Glycine max (L.) Merr.] yield increased at a rate of 23.4 kg ha⁻¹ yr⁻¹ between 1924 and 2010 due to a combination of genetic improvements, agronomic technologies, and climatic changes. To estimate annual rates of genetic yield gain in three northern U.S. soybean maturity groups (MGs) and determine if these estimates are influenced by cropping history, 45 MG II, 40 MG III, and 45 MG IV cultivars released between 1923 and 2008 were evaluated in split-plot trials conducted in Illinois in 2010 where the main plot prior cropping treatments were either 11 yr of continuous corn (Zea mays L.) or 11 yr of a soybean–corn rotation. The experiment-wide genetic yield gain estimate was 22.8 kg ha⁻¹ yr⁻¹ and, after covariate adjustment of yields for maturity, the estimate was 19.8 kg ha⁻¹ yr⁻¹. These estimates show that soybean genetic yield potential has been a large contributor to the rate of on-farm yield improvement. The rate of yield gain estimates were not significantly different (P = 0.38) between the two cropping history treatments on an experiment-wide basis or at the MG III and MG IV individual locations but were significantly different at the MG II locations, where yield gain for soybean following continuous corn was significantly greater compared with the soybean–corn rotation treatment. Modern cultivars were not observed to be able to close the yield gap between the two cropping history treatments used in this experiment.

planting date is a commonly manipulated man - agement practice in soybean ( Glycine max (L.) Merr.) production; however, the impacts of past and ongoing agronomic improvements, such as earlier planting, on genetic yield improvement and associated changes in seed protein and oil have not been evaluated. The objectives of this study were to determine if a 30-d difference in planting date affected measured rates of genetic improvement in (i) yield, (ii) seed mass, and (iii) seed protein and oil in the midwestern United States. research was conducted at Arlington, WI, Urbana, IL, and Lafayette, IN, during 2010 and 2011, using 59 Maturity Group (MG) II cul - tivars (released 1928-2008) at Wisconsin, and 57 MG III cultivars (released 1923-2007) at Illi - nois and Indiana, with targeted planting dates of 1 May and 1 June. Earlier planting provided higher yields (+3.1 kg ha −1 yr −1 ) than late planting in MG III soybean. Seed protein concentration decreased linearly over cultivar year of release at a rate of 0.191 (± 0.069) g kg −1 yr −1 for MG II, and 0.242 (± 0.063) g kg −1 yr −1 for MG III. Seed oil concentration increased over year of release at a rate of 0.142 (± 0.037) g kg −1 yr −1 for MG II, and 0.127 (± 0.039) g kg −1 yr −1 for MG III. The interaction between planting date and cultivar year of release for MG III yield suggested that the trend toward earlier planting is one agro - nomic improvement that, when coupled with genetic improvement, has provided a syner - gistic increase in on-farm soybean yields in the midwestern United States.

Knowledge of soybean [Glycine max (L.) Merr.] primary, secondary, and tertiary root tip locations in the soil vs. seasonal time would enhance modeling of soybean development. The seasonal progression of root tip development and shoot phenology was evaluated in situ using an imaging device inserted into minirhizotron tubes installed in the soil at an in-row 30° angle. Primary root tip extension was linear (i.e., 1.5 and 1.2 cm d⁻¹ each year) until the full-seed stage. Emergent 5-mm secondary roots were routinely detected about 10-cm above the primary root tip, and thus present in a soil layer 11 d after the primary root tip had passed through that layer. Secondary roots followed a similar temporal pattern. Primary root tip location in the soil paralleled a 17°C soil temperature isoline. The 3.7-d phyllochron of main-stem node accrual between first node and seed fill may be a calibratable proxy for inferring correspondent root tip depths.