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Introduction: While globally appreciated for reliable, intensification-friendly phenotypes, modern corn (Zea mays L.) genotypes retain crop plasticity potential. For example, weather and heterogeneous field conditions can overcome phenotype uniformity and facilitate tiller expression. Such plasticity may be of interest in restrictive or otherwise variable environments around the world, where corn production is steadily expanding. No substantial effort has been made in available literature to predict tiller development in field scenarios, which could provide insight on corn plasticity capabilities and drivers. Therefore, the objectives of this investigation are as follows: 1) identify environment, management, or combinations of these factors key to accurately predict tiller density dynamics in corn; and 2) test outof-season prediction accuracy for identified factors. Methods: Replicated field trials were conducted in 17 diverse site-years in Kansas (United States) during the 2019, 2020, and 2021 seasons. Two modern corn genotypes were evaluated with target plant densities of 25000, 42000, and 60000 plants ha-1. Environmental, phenological, and morphological data were recorded and evaluated with generalized additive models. Results: Plant density interactions with cumulative growing degree days, photothermal quotient, mean minimum and maximum daily temperatures, cumulative vapor pressure deficit, soil nitrate, and soil phosphorus were identified as important predictive factors of tiller density. Many of these factors had stark non-limiting thresholds. Factors impacting growth rates and photosynthesis (specifically vapor pressure deficit and maximum temperatures) were most sensitive to changes in plant density. Out-of-season prediction errors were seasonally variable, highlighting model limitations due to training datasets. Discussion: This study demonstrates that tillering is a predictable plasticity mechanism in corn, and therefore could be incorporated into decision tools for restrictive growing regions. While useful for diagnostics, these models are limited in forecast utility and should be coupled with appropriate decision theory and risk assessments for producers in climatically and socioeconomically vulnerable environments. [ABSTRACT FROM AUTHOR]

Introduction: Crop plasticity is fundamental to sustainability discussions in production agriculture. Modern corn (Zea mays L.) genetics can compensate yield determinants to a small degree, but plasticity mechanisms have been masked by breeder selection and plant density management preferences. While tillers are a well-known source of plasticity in cereal crops, the functional trade-offs of tiller expression to the hierarchical yield formation process in corn are unknown. This investigation aimed to further dissect the consequences of tiller expression on corn yield component determination and plasticity in a range of environments from two plant fraction perspectives -- i) main stalks only, considering potential functional trade-offs due to tiller expression; and ii) comprehensive (main stalk plus tillers). Methods: This multi-seasonal study considered a dataset of 17 site-years across Kansas, United States. Replicated field trials evaluated tiller presence (removed or intact) in two hybrids (P0657AM and P0805AM) at three target plant densities (25000, 42000, and 60000 plants ha-1). Record of ears and kernels per unit area and kernel weight were collected separately for both main stalks and tillers in each plot. Results: Indicated tiller contributions impacted the plasticity of yield components in evaluated genotypes. Ear number and kernel number per area were less dependent on plant density, but kernel number remained key to yield stability. Although ear number was less related to yield stability, ear source and type were significant yield predictors, with tiller axillary ears as stronger contributors than main stalk secondary ears in high-yielding environments. Discussions: Certainly, managing for the most main stalk primary ears possible -- that is, optimizing the plant density (which consequently reduces tiller expression), is desirable to maximize yields. However, the demonstrated escape from the deterministic hierarchy of corn yield formation may offer avenues to reduce corn management dependence on a seasonally variable optimum plant density, which cannot be remediated mid-season. [ABSTRACT FROM AUTHOR]

Studies at a regional scale suggest that although maize (Zea mays L.) yield increased substantially, sensitivity to water deficit also increased concurrently with crop improvement. This study assessed changes in yield and yield stability after two decades of breeding by evaluating two cohorts of hybrids released by the AQUAmax program and comparing them to a non‐AQUAmax control. Studies were conducted in 2019 and 2020 seasons at four sites under well‐watered, moderate and severe stress conditions. Plant densities varied from 2.5 to 12.5 pl m−2. AQUAmax hybrids yielded more than non‐AQUAmax hybrids under water deficit conditions with the magnitude of the difference dependent on plant density. The sensitivity in yield across environments with a wide range of total crop evapotranspiration (350–800 mm) was lower for AQUAmax than non‐AQUAmax, so both yield and yield under stress conditions (yield stability), were enhanced for AQUAmax hybrids. The median differences between observed yields and the 80% quantile yield–evapotranspiration front were 379 for AQUAmax and 427 g m−2 for non‐AQUAmax hybrids. We conclude that deliberate selection of hybrids for yield performance under water deficit underpinned the sustained improvement in yield stability after two decades of drought breeding. Future research should focus on understanding the root causes for the suboptimal utilization of drought tolerant maize for the diversity of environments at a regional scale. Core Ideas: Yield improvement for drought is critical to enable a circular bioeconomy.Maize susceptibility to drought at regional scale is increasing with crop improvement.Genotype × management technology (G×M‐tech) is not the root cause of the higher instability.Socioeconomic factors and policy may underpin suboptimal use of G×M‐tech at regional scale. [ABSTRACT FROM AUTHOR]

One of the main challenges of using low plant densities in restrictive and variable environments is to maximize the use of resources in better‐than‐expected years. In this context, tillering could be an alternative to increase reproductive and vegetative plasticity. The objectives of this study were to (a) characterize the correlation between environmental conditions, tiller traits, crop grain yield, and grain yield advantages due to tillers; (b) determine the grain yield response to tillering (i.e., grain yield difference between tillered and nontillered crops) for a wide range of environments; and (c) to evaluate the effect of tiller presence on grain yield of the main shoot, considering its effect on the apical and subapical ears. Tillered and nontillered crops were evaluated under rainfed conditions during two seasons (2018–2019 and 2019–2020). These experiments were carried out at 11 sites in the southern Argentinean Pampas, varying sowing date (22 Oct. to 5 Dec.), plant density (2–3 plants m−2), genotype (AX7784 and AX7761), and soil depth. Grain yield (3.2–11.9 Mg ha−1) was correlated with tillers productivity, mainly explained by postflowering precipitations. The contribution of tillers to grain yield was more proportional than their consequent yield depression at the main shoot. Tillers either increased (3 sites) or maintained (8 sites) grain yield, and their contribution increased as the environment improved without any detrimental effect in the most restrictive environments. Tillering has the potential for increasing resource (radiation, water, nitrogen) use efficiency under low plant density strategies adopted for restrictive environments. Core Ideas: Tillers either increased (3 sites) or maintained (8 sites) crop yield.Grain yield was correlated with tillers productivity, mainly explained by postflowering precipitations.Tiller contribution to grain yield was greater than the consequent yield depression of the main shoot.Tiller contribution to yield increased as the environment improved (high yield).Tillering has the potential for increasing resource use efficiency under low plant density strategies. [ABSTRACT FROM AUTHOR]

Increased soybean (Glycine max L. Merril) seed costs have motivated interest in reduced seeding rates to improve profitability while maintaining or increasing yield. However, little is known about the effect of early-season plant-to-plant spatial uniformity on the yield of modern soybean varieties planted at reduced seeding rates. The objectives of this study were to (i) investigate traditional and devise new metrics for characterizing early-season plant-to-plant spatial uniformity, (ii) identify the best metrics correlating plant-to-plant spatial uniformity and soybean yield, and (iii) evaluate those metrics at different seeding rate (and achieved plant density) levels and yield environments. Soybean trials planted in 2019 and 2020 compared seeding rates of 160, 215, 270, and 321 thousand seeds ha−1 planted with two different planters, Max Emerge and Exact Emerge, in rainfed and irrigated conditions in the United States (US). In addition, trials comparing seeding rates of 100, 230, 360, and 550 thousand seeds ha−1 were conducted in Argentina (Arg) in 2019 and 2020. Achieved plant density, grain yield, and early-season plant-to-plant spacing (and calculated metrics) were measured in all trials. All site-years were separated into low- (2.7 Mg ha−1), medium- (3 Mg ha−1), and high- (4.3 Mg ha−1) yielding environments, and the tested seeding rates were separated into low (< 200 seeds m−2), medium (200–300 seeds m−2), and high (> 300 seeds m−2) levels. Out of the 13 metrics of spatial uniformity, standard deviation (sd) of spacing and of achieved versus targeted evenness index (herein termed as ATEI, observed to theoretical ratio of plant spacing) showed the greatest correlation with soybean yield in US trials (R2 = 0.26 and 0.32, respectively). However, only the ATEI sd, with increases denoting less uniform spacing, exhibited a consistent relationship with yield in both US and Arg trials. The effect of spatial uniformity (ATEI sd) on soybean yield differed by yield environment. Increases in ATEI sd (values > 1) negatively impacted soybean yields in both low- and medium-yield environments, and in achieved plant densities below 200 thousand plants ha−1. High-yielding environments were unaffected by variations in spatial uniformity and plant density levels. Our study provides new insights into the effect of early-season plant-to-plant spatial uniformity on soybean yields, as influenced by yield environments and reduced plant densities. [ABSTRACT FROM AUTHOR]

The light attenuation process within a plant canopy defines energy capture and vertical distribution of light and nitrogen (N). The vertical light distribution can be quantitatively described with the extinction coefficient (k), which associates the fraction of intercepted photosynthetically active radiation (fPARi) with the leaf area index (LAI). Lower values of k correspond to upright leaves and homogeneous vertical light distribution, increasing radiation use efficiency (RUE). Yield gains in maize (Zea mays L.) were accompanied by increases in optimum plant density and leaf erectness. Thus, the yield-driven breeding programs and management changes, such as reduced row spacing, selected a more erect leaf habit under different maize production systems (e.g. China and the USA). In this study, data from Argentina revealed that k decreased at a rate of 1.1% year–1 since 1989, regardless of plant density and in agreement with Chinese reports (1.0% year–1 since 1981). A reliable assessment of changes in k over time is critical for predicting (i) modifications in resource use efficiency (e.g. radiation, water, and N), improving estimations derived from crop simulation models; (ii) differences in productivity caused by management practices; and (iii) limitations to further exploit this trait with breeding. [ABSTRACT FROM AUTHOR]

Long‐term selection in maize (Zea mays L.) favored single‐stalked phenotypes limiting vegetative growth. However, reduced plant densities create conducive environments to the expression of vegetative branches called tillers. Tiller expression has motivated discussions about its yield effect in variable environments, but tiller research is lacking for modern corn genotypes. The objectives of this study were to (a) quantify the relative importance of management, environment, and interactions on the yield effect of tiller expression for two modern genotypes; (b) understand effects of observed tiller density, plant density, and their interaction on yield; and (c) identify key environmental determinants of yield response to tiller density in modern genotypes. In 10 environmentally diverse site‐years across Kansas, tiller presence and removal were evaluated in two commercial corn hybrids (P0657AM and P0805AM) across three target plant density levels (25,000, 42,000, and 60,000 plants ha−1). Yields were increased or unaffected by greater plant densities and tiller presence within site‐years. Environments varied in yield responsiveness to tiller density, but fine‐tuning plant density was needed to maximize yields. Sites with yields most responsive to tiller density were characterized by good soil properties and photothermal quotient values (e.g., soils with high organic matter and climates with greater solar radiation and cooler temperatures). Favorable growing conditions can be exploited by plasticity traits such as tillering in unpredictable environments with annually variable optimum plant densities while limiting potential yield loss and producer risk due to disproportionate plant density. Core Ideas: Tiller presence never reduced yield in evaluated environments and plant densities.In favorable scenarios, corn tillers have plant density compensation potential.Plant density adjustment was necessary to maximize yield for all environments.Corn tillers have plasticity potential to optimize defensive management strategies. [ABSTRACT FROM AUTHOR]

Wheat (Triticum aestivum L.) grain yield response to plant density is inconsistent, and the mechanisms driving this response are unclear. A better understanding of the factors governing this relationship could improve plant density recommendations according to specific environmental and genetics characteristics. Therefore, the aims of this paper were to: i) execute a synthesis-analysis of existing literature related to yield-plant density relationship to provide an indication of the need for different agronomic optimum plant density (AOPD) in different yield environments (YEs), and ii) explore a data set of field research studies conducted in Kansas (USA) on yield response to plant density to determine the AOPD at different YEs, evaluate the effect of tillering potential (TP) on the AOPD, and explain changes in AOPD via variations in wheat yield components. Major findings of this study are: i) the synthesis-analysis portrayed new insights of differences in AOPD at varying YEs, reducing the AOPD as the attainable yield increases (with AOPD moving from 397 pl m-2 for the low YE to 191 pl m-2 for the high YE); ii) the field dataset confirmed the trend observed in the synthesis-analysis but expanded on the physiological mechanisms underpinning the yield response to plant density for wheat, mainly highlighting the following points: a) high TP reduces the AOPD mainly in high and low YEs, b) at canopy-scale, both final number of heads and kernels per square meter were the main factors improving yield response to plant density under high TP, c) under varying YEs, at per-plant-scale, a compensation between heads per plant and kernels per head was the main factor contributing to yield with different TP. [ABSTRACT FROM AUTHOR]

Inconsistent soybean [Glycine max (L.) Merr.] seed yield response to plant density has been previously reported. Moreover, recent economic and productive circumstances have caused interest in within-field variation of the agronomic optimal plant density (AOPD) for soybean. Thus, the objectives of this study were to: (i) determine the AOPD by yield environments (YE) and (ii) study variations in yield components (seed number and weight) related to the changes in seed yield response to plant density for soybean in North America. During 2013 and 2014, a total of 78 yield-to-plant density responses were evaluated in different regions of the United States and Canada. A soybean database evaluating multiple seeding rates ranging from 170,000 to 670,000 seeds ha-1 was collected, including final number of plants, seed yield, and its components (seed number and weight). The data was classified in YEs: low (LYE, <4 Mg ha-1), medium (MYE, 4-4.3 Mg ha-1), and high (HYE, >4.3 Mg ha-1). The main outcomes were: (i) AOPD increased by 24% from HYE to LYE, (ii) per-plant yield increased due to a decrease in plant density: HYE > MYE > LYE, and (iii) per-plant yield was mainly driven by seed number across plant densities within a YE, but both yield components influenced per-plant yield across YEs. This study presents the first attempt to investigate the seed yieldto- plant density relationship via the understanding of plant establishment and yield components and by exploring the influence of weather variables defining soybean YEs. [ABSTRACT FROM AUTHOR]

Understanding the relationship of corn (Zea mays L.) yield responses to plant density and nitrogen (N) fertilization is critical to production decisions. The main objectives of this study were to (i) evaluate yield responses to plant density and fertilizer N rate at varying yields adjusting models considering a spatial component, (ii) perform a validation for the fitted models with an independent dataset, and (iii) identify key statistical parameters for the yield data distribution governing response models. Analyses were conducted with information from seven fields with 21 studies (one study per yield environment, with three environments per field) conducted from 2009 to 2017 in southern Brazil with geospatial data collected to evaluate yield response to plant density and fertilizer N rates (28911 data points) and one additional database with 12 field studies conducted from 2012 to 2015 in the US Midwest (1773 data points). Databases were divided into training and validation datasets. Field experiments evaluating both plant density and N rate were selected as training dataset. Key research findings were (i) yield-factor response models were dependent on yield environment and within a yield environment those models remained constant regardless the year, country, and hybrid for all evaluated fields, (ii) statistical models considering spatial correlation of the random errors outperformed those considering errors independent and identically distributed and, (iii) yield distribution with comparable 50% interquartile range and mode portrayed similar yield-factor relationship. In summary, fitting spatial yield-density models considering yield data distribution is critical to upscale site-specific models to larger spatial domains. [ABSTRACT FROM AUTHOR]

Untangling the most relevant factors underpinning sorghum (Sorghum bicolor L.) yields is critical to identify, within the target environments, the best management practices to increase production and reverse the negative trend in planted area across the United States (US). This study aims to provide new insights of the effects of environment (E) and management (M) on sorghum yields. Specific goals for this study were linked to i) describe the relevance of E factors (mainly heat and drought), and ii) explore the effect of M component on yield variability across different US sorghum producing regions. The US National Sorghum Producer (NSP) Yield Contest self-reported farmer-based dataset, spanning 24 states from 2013 to 2017 period (n = 782) was split in yield terciles. Three different approaches were employed to independently explore each yield tercile: i) crop modeling simulation to quantify the impact of heat and drought on yield, ii) conditional inference tree to rank the most relevant management practices, and lastly, iii) fuzzy C means spatial clustering to identify critical management practices linked to geographical yield clusters across crop regions. The E component accounted for nearly half of the sorghum yield variation for the low-yielding group (< 7.6 Mg ha-1), while the M factor (mainly planting date and irrigation) played a key role for the high-yielding group (> 9.7 Mg ha-1). Geographically-relevant yield clusters identified three main regions, i) east (with earlier planting dates, early May, and high N fertilization rates), ii) west (with irrigation as the main factor) within the continental area, and iii) coastal zone (with planting dates before April 1, southeast Texas). For the low-yielding group, row spacing, and plant density were key variables accounting for yield variation. These outcomes provide foundational insights on the major E and M factors limiting attainable yields within farmers targeting high yields in the main US sorghum producing regions. [Display omitted] • 782 Sorghum Producer Contest entries to quantify the effect of management by environment on yields. • Environmental factors explained 41 % of yield variation for the low-yielding group (<7.6Mg/ha). • Management, mainly irrigation, was relevant for the high-yielding group (>9.7Mg/ha). • Productive systems in west US employed irrigation and lower plant densities to attain higher yields. [ABSTRACT FROM AUTHOR]

Identifying an optimal plant density is a critical management decision for corn (Zea mays L.) production. The main objectives of this study were to: (i) investigate the grain yield responses to plant density (yield-density relationship), (ii) identify best fitted yield-density response curves, and (iii) explore genotype (G) x environment (E) interaction effect on yield-density response models. Analysis was conducted on meta-data (124,374 observations) gathered from 22 US states and 2 Canadian provinces, diverse sites (E), for years from 2000-2014 on multiple hybrids (G). Yield data were further grouped into four yield environments (low [LY], <7 Mg ha-1; medium [MY], 7-10 Mg ha-1; high [HY], 10-13 Mg ha-1; and very high [VHY], >13 Mg ha-1 yielding groups). Primary outcomes from this analysis were: (1) strong G x E interaction; (2) a quadratic model best fitted yield-density relationship; (3) four contrasting yield-density responses identified as dominant in each yield productivity environment, i.e., a declining, a constant, an increasing, and ever-increasing type; (4) the yield productivity environment varied for the different corn comparative relative maturity (CRM) groups, i.e., the LY environment for long-maturing hybrids matched with a MY or HY environment for short maturing hybrids; and (5) maximum yielding plant density (MYPD) was lower but maximum yield was greater for long- versus short-maturing hybrids. In summary, optimal plant density should be decided based on detailed G x E analysis of production conditions that include factors such as CRM, yield productivity environment (weather-soil x management practices), and site information. [ABSTRACT FROM AUTHOR]

Improved plant N utilization and partitioning is critical for future improvements in maize (Zea mays L.) grain yield (GY). The overall research objective was to gain understanding of the physiological mechanisms underpinning biomass (BM), N uptake partitioning, and GY processes during the reproductive period for two maize hybrids grown at varying plant density (PD) (low is 54,000 plant ha-1, medium is 79,000 plants ha-1, and high is 104,000 plants ha-1) and N inputs (low is 0 kg N ha-1, medium is 112 kg N ha-1, and high is 224 kg N ha-1) over four site- years. At the community level, maize GY was maximized in both genotypes at the medium PD and highest N rate. At maturity, grain harvest index improved as the whole-plant N uptake increased following a linear-plateau model and, for N allocation, both grain and shoot N concentrations increased similarly as BM increased. Around flowering (±15 d), dry mass and N partitioning rates were unmodified by treatment factors. Treatment factors only marginally influenced potential kernel number near flowering. Allometric analyses confirmed a lack of treatment impact on whole-plant N uptake and N remobilization coefficients. Greater reproductive- stage N uptake was associated with superior ear strength (kernel number and weight) and late shoot N remobilization, but GY was also positively related to vegetative-stage N uptake. Future research should identify genotypic variation for overcoming the documented N uptake trade-off mechanisms (vegetative-stage N uptake vs. shoot N remobilization) as related to the maize GY improvement process. [ABSTRACT FROM AUTHOR]

Maize (Zea mays L.) yields have advanced through breeding complemented with evolving management technologies including plant density (PD) and macronutrient fertilizer inputs. Little is known about management-induced changes in plant uptake or allocation of nutrients other than macronutrients. Therefore, impacts of both PD and N rate at three levels (low, medium, and high) on Ca, Mg, and micronutrient partitioning (for pertinent plant organs at six growth stages) were investigated at four environments in Indiana. Grain Ca, Mg, Fe, and Zn contents at maturity were primarily influenced by N rate, while the PD X N rate interaction influenced those of Mn and Cu. At the whole-plant scale, PD and N rate significantly influenced all nutrient contents, and vegetative-stage nutrient accumulation averaged 91% (Ca), 51% (Fe), 47% (Zn), and 73% (Mn, Mg, and Cu) of corresponding nutrient contents at maturity. During the vegetative phase, three modes of leaf vs. stem nutrient partitioning were: (i) preferential allocation of Mg and Zn to stems; (ii) preferential allocation of Fe and Ca to leaves; and (iii) isometric partitioning of Cu and Mn. Isometric nutrient concentration patterns between Mg and Zn were documented in leaf, stem (vegetative phase), and ear (reproductive phase). Early-reproductive-stage nutrient partitioning from plant to ear was greatest for Zn and Mg and mirrored their respective harvest indices (HIs) at maturity. Nutrient HIs, concentrations (grain + stover), and internal efficiencies at maturity were positively impacted by N rate but negatively by PD. Reliable micronutrient requirement estimations for maize under diverse management and yield levels help inform future balanced-nutrient input decisions. [ABSTRACT FROM AUTHOR]

From a physiological perspective, field studies that quantify the influence of plant density (PD) and N rate on biomass (BM) and N uptake are needed to build more functional partitioning models for maize (Zea mays L.). The overall goal was to quantify the effects of maize hybrid (two genotypes), PD (low = 54,000; medium = 79,000 and high = 104,000 pl ha-1), and N rate (low = 0, medium = 112 and high = 224 kg N ha-1) on the organ-specific dry mass and N allocation, and on the resource capture (ratio of leaf area index [LAI] to BM), and resource use efficiency (ratio of N uptake to LAI) parameters, during the vegetative phase at four site-years. Allometric analyses revealed that the resource capture was primarily affected by the PD, and the resource use efficiency by the N rate. The stoichiometry between leaf and stem dry mass was unaffected by the treatment factors, but no isometry was documented (greater BM to the stem than leaf). The N rate primarily modified the leaf and stem N concentrations (0N vs. 112- 224N). With respect to resource use efficiency, modifications in the leaf N content per unit of LAI were joint outcomes of changes in leaf mass and %N (N concentration). In contrast, physiological changes in the stem N content per unit of LAI were more dependent on changes in the stem %N (greater N storage capacity) rather than in the stem mass. Intensifying competition with neighboring plants reduced per-plant mass similarly in both leaf and stem structures, and improving N supply was proportionately more beneficial in maintaining plant mass and stem %N at high PD during vegetative growth. [ABSTRACT FROM AUTHOR]

Understanding nutrient balances in changing cropping systems is critical to appropriately adjust agronomic recommendations and inform breeding efforts to increase nutrient efficiencies. Research to determine the season-long P, K, and S uptake and partitioning dynamics in maize (Zea mays L.) as affected by low, medium, and high plant density (PD) and N rate factors and their interactions was conducted over four site-years in Indiana. Plant nutrient contents at maturity responded predominantly to N rate. Relative nutrient contents at silking compared with those at maturity were 47% for P, 100% for K, and 58% for S. Concentrations of P, K, and S varied less in leaf vs. stem (vegetative stage) and in ear vs. shoot (reproductive stage). Equivalent stoichiometric ratios were documented for N and S partitioning in leaf, stem, and ear components. The PD and N rate treatments did not modify P, K, and S nutrient partitioning to plant components during vegetative or reproductive periods (except for an N rate effect on leaf vs. stem P partitioning). Near silking, relative nutrient partitioning to the ear followed the order P > S > K. This mimicked the nutrient harvest indices observed at maturity, suggesting genetic modulation. Ratios of N to P, K, and S in whole-plant tissues were influenced by N content changes in response to N rate but not by PD. As the season progressed, PD and N rates changed the absolute P, K, and S quantities (primarily reflecting biomass responses) but had little influence on nutrient ratios. [ABSTRACT FROM AUTHOR]

Twin-row planting systems in maize (Zea mays L.) have been proposed as an alternative spatial arrangement that should theoretically decrease plant-to-plant competition, alleviate crop crowding stress and improve yields. Uncertainty remains, however, as to whether twin rows are a feasible option to increase plant densities and improve grain yields. Three hybrids (DKC62-54, DKC61-19, and DKC57-66) were grown from 2009 to 2011 to evaluate the individual and interacting effects of plant density (PD1 = 69,000; PD2 = 81,000; PD3 = 93,000; and P D 4 = 105,000 plants [pi] ha-1 ) and spatial configuration (conventional single 76-cm row width vs. 20-cm twin rows spaced 76-cm between paired-rows) on dark prairie soil in West- Central Indiana. The primary research objectives were to determine (i) whether the twin-row spatial arrangement permits higher optimum plant densities, (ii) whether hybrids vary in their response to at win-row arrangement, and (iii) diverse morphophysiological trait responses to density and spatial treatments. Twin rows never yielded significantly more than single rows at any plant density or hybrid combination in any year of this study. Furthermore, there was no evidence that grain yield-optimizing plant densities were any higher with twin vs. single rows in any hybrid. Twin rows slightly increased leaf area index (LAI) at silk emergence stage in 2010 (mean LAI = 4.8) and 2011 (mean LAI = 4.0), but not in 2009 (mean LAI = 4.4). Despite higher plant spacing variation, radiation interception was initially favored by earlier canopy closure with twin-row planting, but the relative radiation-interception advantage declined as plant density increased and at a later vegetative stage. [ABSTRACT FROM AUTHOR]

Abstract: Over the past 3 decades, the study of various mechanisms involved in maize grain yield (GY) formation and its relationship with nitrogen (N) uptake dynamics has been increasingly acknowledged in the scientific literature. However, few studies have combined investigations of GY response to N fertilizer with detailed physiologically based analyses of plant N dynamics such as N uptake quantities, timing, and (or) partitioning – and the complex interactions of those with specific genotypes (G), management practices (M), and (or) production environments (E). Limited reporting of both N and yield dynamics at plant-component, individual-plant, and community levels has contributed to a considerable knowledge gap as to whether the physiological mechanisms that govern maize plant N dynamics and their relationship with GY formation have changed with time. We therefore undertook a comprehensive review to discern trends in physiological aspects of maize response to changing plant densities and fertilizer N rates (M components) under the umbrella of evolving G×E interactions. We reviewed 100 published and unpublished papers based on field experiments which consistently reported total plant N uptake at maturity and maize GY (frequently among other physiological variables). Our analyses were limited nearly exclusively to experiments involving hybrid (as distinct from inbred) response to M input levels where plant density data was available. Dissection of the complex interactions among years, plant densities and N rates began with division of treatment mean data (close to ∼3000 individual points) into two time periods defined by year(s) of the original research: (i) studies from 1940 to 1990 – “Old Era” and, (ii) studies from 1991 to 2011 – “New Era”. For the Old Era, maize GY averaged 7.2Mgha−1 at a mean plant density of 5.6plm−2 with a total plant N uptake of 152kgNha−1, a grain harvest index (HI) of 48% and N harvest index (NHI) of 63%. For the New Era, maize GY averaged 9.0Mgha−1 at a mean plant density of 7.1plm−2, total plant N uptake of 170kgNha−1, a grain HI of 50% and a NHI of 64%. The most striking findings in terms of overall GY and plant N uptake were: (1) on a per-unit-area basis, both potential GY and NIE (GY/N uptake) increased from Old to New Era at comparable N uptake levels, and (2) on a per-plant basis, total plant N uptake at maturity had not changed between Eras despite increased plant density in the New Era genotypes. Other important findings in terms of plant growth and component partitioning responses to N were (i) a consistently strong dependency between dry matter and N allocation to the ear organ in both Eras; (ii) higher total plant biomass (BM) accumulation and N uptake, on an absolute basis, during the post-silking period with New Era genotypes accompanied by relatively smaller changes in HI and NHI; (iii) a strong correlation between plant N uptake at silking time and per-plant GY and its components in both Eras; (iv) New Era (56.0kg GYgrainkg−1 N) was primarily associated with reduced grain %N, and to a minor degree with NHI gains; and (v) New Era genotypes showed higher tolerance to N deficiency stress (higher GY when no N fertilizer was applied), and larger GY response per unit of N applied, relative to Old Era hybrids. This improved understanding of the physiological factors underlying progress in maize yield response to N over time, within the context of changing G×E×M factors, serves to help guide maize programs focused on achieving further improvements in N use efficiency. [Copyright &y& Elsevier]

Abstract: Nitrogen (N) use efficiency (NUE), defined as grain produced per unit of fertilizer N applied, is difficult to predict for specific maize (Zea mays L.) genotypes and environments because of possible significant interactions between different management practices (e.g., plant density and N fertilization rate or timing). The main research objective of this study was to utilize a quantitative framework to better understand the physiological mechanisms that govern N dynamics in maize plants at varying plant densities and N rates. Paired near-isogenic hybrids [i.e., with/without transgenic corn rootworm (Diabrotica sp.) resistance] were grown at two locations to investigate the individual and interacting effects of plant density (low—54,000; medium—79,000; and high—104,000plha−1) and sidedress N fertilization rate (low—0; medium—165; and high—330kgNha−1) on maize NUE and associated physiological responses. Total aboveground biomass (per unit area basis) was fractionated and both dry matter and N uptake were measured at four developmental stages (V14, R1, R3 and R6). Both plant density and N rate affected growth parameters and grain yield in this study, but hybrid effects were negligible. As expected, total aboveground biomass and N content were highly correlated at the V14 stage. However, biomass gain was not the only factor driving vegetative N uptake, for although N-fertilized maize exhibited higher shoot N concentrations than N-unfertilized maize, the former and latter had similar total aboveground biomass at V14. At the R1 stage, both plant density and N rate strongly impacted the ratio of total aboveground N content to green leaf area index (LAI), with the ratio declining with increases in plant density and decreases in N rate. Higher plant densities substantially increased pre-silking N uptake, but had relatively minor impact on post-silking N uptake for hybrids at both locations. Treatment differences for grain yield were more strongly associated with differences in R6 total biomass than in harvest index (HI) (for which values never exceeded 0.54). Total aboveground biomass accumulated between R1 and R6 rose with increasing plant density and N rate, a phenomenon that was positively associated with greater crop growth rate (CGR) and nitrogen uptake rate (NUR) during the critical period bracketing silking. Average NUE was similar at both locations. Higher plant densities increased NUE for both medium and high N rates, but only when plant density positively influenced both the N recovery efficiency (NRE) and N internal efficiency (NIE) of maize plants. Thus plant density-driven increases in N uptake by shoot and/or ear components were not enough, by themselves, to increase NUE. [Copyright &y& Elsevier]

• We studied how nitrogen internal efficiency (NIE) is regulated by prolificacy in maize crops. • Crops expressed high prolificacy values at low density with high N supply. • NIE variations for prolific plants/crops were mainly determined by nitrogen harvest index. • Higher yields, total N uptakes and NIEs were recorded for prolific plants. • Prolificacy stabilized crops yields and NIE in response to environmental improvements. In maize (Zea mays L.) crop, "defensive practices" such as low plant densities and limited nitrogen (N) fertilization have begun to be progressively adopted in limited-production regions of the world. At low plant density, the expression of prolificacy (i.e., more than one fertile ear per plant) can stabilize maize production. Considering N as the main limiting resource, yield can be defined as a function of i) total N uptake and the efficiency to produce yield from total N uptake, i.e., N internal efficiency (NIE) or ii) total N uptake, N harvest index (NHI) and grain N concentration. The objective of this work was to describe changes in NIE and their related components regulated by prolificacy at both canopy- and plant- scales. Field studies were carried out in Buenos Aires, Argentina, during 2015–2016 and 2016–2017 seasons. Treatments were combinations of two N supplies (N-: 0 and N+: 200 kg N ha−1), two plant densities (4 and 8 plants m−2) and five maize hybrids released during the last four decades. High N supply positively impacted yield per unit area (ca. 45 %) by increasing kernel number (ca. 33 %), kernel weight (ca. 18 %) and harvest index (ca. 12 %). At low plant density, high N supply expressed the highest prolificacy (mean 1.65 ears plant−1). The older hybrids displayed the highest prolificacy (mean 1.30 ears plant−1) and the lowest yield per unit area (mean 792 g m−2), but showed the greatest grain N concentration (mean 15.1 g kg−1). However, recent hybrids resulted in medium prolificacy (mean 1.11 ears plant−1) with the highest yield per unit area (mean 910 g m−2), but with the lowest grain N concentration (mean 13.6 g kg−1). The different pattern of grain N concentration among hybrids coupled with similar NHI (mean 0.80) led to the lowest NIE of the most prolific hybrids (mean 55 g yield g N−1) relative to the less prolific ones (mean 60 g yield g N−1). Variations in NIE among genotypes and plant densities were negatively influenced by grain N concentration under N- and under N+ only when prolificacy = 1. Under N+ with prolificacy > 1, NIE variations were positively determined by NHI. For all genotypes, higher yield per plant (mean 30 %) and total N uptake (mean 25 %) were recorded for the prolific plants, reflected in the greater absolute NIE (mean 5 %). For the oldest hybrid differences between prolific and non-prolific plants in NIE (mean 46.8 vs 41.8 g yield g N-1) were greater than those recorded for the other genotypes due to the higher impact of prolificacy on yield per plant (mean 51 %) than on total N uptake (mean 26 %). Over time, improvements in NIE primarily occurred from greater yield of the apical ear. Overall, the expression of prolificacy appears to be an adequate strategy to stabilize maize production in response to improvements of environmental conditions, specially addressed by low plant density and high N supply. [ABSTRACT FROM AUTHOR]

Targeting the right agronomic optimum plant density (AOPD) for maize (Zea mays L.) is a critical management decision, but even more when the seed cost and grain selling price are accounted for, i.e. economic OPD (EOPD). From the perspective of improving those estimates, past studies have focused on utilizing a Frequentist (classical) approach for obtaining single-point estimates for the yield-density models. Alternative analysis models such as Bayesian computational methods can provide more reliable estimation for AOPD, EOPD and yield at those optimal densities and better quantify the scope of uncertainty and variability that may be in the data. Thus, the aims of this research were to (i) quantify AOPD, EOPD and yield at those plant densities, (ii) obtain and compare clusters of yield-density for different attainable yields and latitudes, and (iii) characterize their influence on EOPD variability under different economic scenarios, i.e. seed cost to corn price ratios. Maize hybrid by seeding rate trials were conducted in 24 US states from 2010 to 2019, in at least one county per state. This study identified common yield-density response curves as well as plant density and yield optimums for 460 site-years. Locations below 40.5 N latitude showed a positive relationship between AOPD and maximum yield, in parallel to the high potential level of productivity. At these latitudes, EOPD depended mostly on the maximum attainable yield. For the northern latitudes, EOPD was not only dependent on the attainable yield but on the cost:price ratio, with high ratios favoring reductions in EOPD at similar yields. A significant contribution from the Bayesian method was realizing that the variability of the estimators for AOPD is sometimes greater than the adjustment accounting for seed cost. Our results point at the differential response across latitudes and commercial relative maturity, as well as the significant uncertainty in the prediction of AOPD, relative to the economic value of the crop and the seed cost adjustments. [ABSTRACT FROM AUTHOR]

Tillering allows grass crops to adapt to their environment by increasing leaf area (source) and grain set (sink). Efficient translocation of water-soluble carbohydrates (WSCs) from tiller biomass is key to match the carbon (C) demand of ears. This ability is still commonly disputed, particularly in corn (Zea mays L.) when tillers are barren. Clarifying these points is imperative to determine tiller utility in corn crops, particularly when low plant densities are employed to manage restrictive environmental conditions with lower yield potential. Therefore, the objectives of the current study are to determine the following: 1) the impact of plant density and tiller presence on corn biomass accumulation, 2) the reproductive efficiency of tillered corn phenotypes considering the harvest index (grain to aboveground biomass ratio), and 3) the changes in stem C storage capacity and remobilization capabilities across tested environment × management scenarios. The database used to accomplish these objectives included six site-years across Kansas, United States; three plant densities (25,000, 42,000, and 60,000 plants ha−1); two genotypes (P0657AM and P0805AM); and two tiller scenarios (intact or removed at stage V10). At these site-years, aboveground biomass and stem samples for WSC were collected at five unique timepoints throughout the season. Tiller presence stabilized aboveground biomass across plant densities and increased the WSC storage capacity in the lowest plant density. Whole plant harvest index was maintained at approximately 0.49 regardless of tiller biomass. Results indicate corn tillers enhanced WSC storage and reduced sink limitations during grain fill in the environments sampled in this study (less water-restrictive conditions). Therefore, biomass and carbohydrate accumulation of tillered phenotypes accompanied the escape from yield dependence on plant density, as evidenced in previous publications from this database. While tillers were not directly responsible for yield efficiency reductions in tillered phenotypes with the environments and metrics evaluated in the current study, more resource-restrictive environments (with less water, for example) could alter this outcome. Additional work on the carbon economy of tillered corn plants in more diverse environments is needed. • Corn tillers provided stability to aboveground biomass production across plant densities. • Tiller biomass was not correlated with whole plant harvest index values. • Tillers provided an additional source of carbohydrates for developing ears. [ABSTRACT FROM AUTHOR]

Crop simulation models are used at the field scale to estimate crop yield potential, optimize current management, and benchmark input-use efficiency. At issue is the ability of crop models to predict local and regional actual yield and total production without need of site-year specific calibration of internal parameters associated with fundamental physiological processes. In this study, a well-validated maize simulation model was used to estimate yield potential for 45 locations across the U.S. Corn Belt, including both irrigated and rainfed environments, during four years (2011–2014) that encompassed diverse weather conditions. Simulations were based on measured weather data, dominant soil properties, and key management practices at each location (including sowing date, hybrid maturity, and plant density). The same set of internal model parameters were used across all site-years. Simulated yields were upscaled from locations to larger spatial domains (county, agricultural district, state, and region), following a bottom-up approach based on a climate zone scheme and distribution of maize harvested area. Simulated yields were compared against actual yields reported at each spatial level, both in absolute terms as well as deviations from long-term averages. Similar comparisons were performed for total maize production, estimated as the product of simulated yields and official statistics on maize harvested area in each year. At county-level, the relationship between simulated and actual yield was better described by a curvilinear model, with decreasing agreement at higher yields (>12 Mg ha −1 ). Comparison of actual and simulated yield anomalies, as estimated from the yearly yield deviations from the long-term actual and simulated average yield, indicated a linear relationship at county-level. In both cases (absolute yields and yield anomalies comparisons), the agreement increased with increasing spatial aggregation (from county to region). An approach based on long-term actual and simulated yields and year-specific simulated yield allowed estimation of actual yield with a high degree of accuracy at county level (RMSE ≤ 18%), even in years with highly favorable weather or severe drought. Estimates of total production, which are of greatest interest to buyers and sellers in the market, were also in close agreement with actual production (RMSE ≤ 22%). The approach proposed here to estimate yield and production can complement other approaches that rely on surveys, field crop cuttings, and empirical statistical methods and serve as basis for in-season yield and production forecasts. [ABSTRACT FROM AUTHOR]

Pearl millet (Pennisetum glaucum R. Br.) water-limited yield gap in Senegal is estimated at 2.2 Mg ha-1. Proper variety and management selection are critical to the sustainable intensification of millet systems. The objectives of this study were to assess the effect of two pearl millet varieties and different crop management (plant density-nutrient rates) treatments on i) biomass yield, ii) grain yield, and iii) farm profit magnitude and responsiveness. The study was conducted on three sites during the 2017, 2018, and 2019 growing seasons with a total of nine site-years. Two varieties, Souna 3 (traditional) and Thialack 2 (dual-purpose), and 48 management treatments including two plant densities (37–74 × 1000 plants ha-1) and 24 different levels of nutrient combinations ranging from 0 to 149 kg ha-1 nitrogen (N, as urea), 0–67 kg ha-1 phosphorus (P 2 O 5 , as double super phosphate), 0–33 kg ha-1 potassium (K 2 O, as potassium chloride) and 0–14 987 kg ha-1 cow manure (M), were tested in all site-years. Thialack 2 presented greater biomass yield in three and grain yield in four site-years (out of nine site-years) relative to Souna 3. Management treatments with greatest magnitude and responsiveness produced on average 4.6 Mg ha-1 biomass yield with averaged nutrient rates of 102–37–26 NPK, 2 Mg ha-1 grain yield with averaged nutrient rates of 91–32–24 NPK, 682 USD ha-1 profit at low biomass and grain prices with averaged nutrient rates of 72–22–21 NPK, and 1183 USD ha-1 profit at high biomass and grain prices with averaged nutrient rates of 83–29–22 NPK, all at 74 thousand plants ha-1, regardless of variety used. Five to seven management practices were able to concurrently optimized biomass yield, grain yield, and farmer profit. Sustainably intensifying pearl millet systems by using improved variety and management practices have great potential to promote greater yields and farm profitability in Senegal. • New pearl millet varieties can be used for biomass and grain production. • Yield response and magnitude is driven by environmental conditions. • Greater plant density and nutrient application rates improve pearl millet yield. • Farm profit can be optimized with greater plant density and modest nutrient rates. • Pearl millet in Senegal has potential to be profitably intensified. [ABSTRACT FROM AUTHOR]