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Nitrogen (N) nutrition index (NNI) is a reliable indicator of plant N status for field crops, but its determination is both labor- and cost-intensive. The utilization of remote sensing approaches for monitoring N, mainly in relevant crops such as of corn (Zea mays L.), will be critical for enhancing effective use of this nutrient. Therefore, the aim of this study was to assess NNI predicted from optical and C-band Synthetic Aperture Radar (C-SAR) satellite data and available soil N (Nav) at different vegetative growth stages for corn crop. Eleven field studies were conducted in the Pampas region (Argentina), applying five fertilizer N rates (0, 60, 120, 180, and 240 kg N ha−1), all at sowing time. Plant samples were collected at sixth-leaf (V6), tenth-leaf (V10), fourteen-leaf (V14), and flowering (R1). Using linear regression models, NNI was best predicted using only optical satellite data from V6 to V14, and integrating optical with C-SAR plus Nav at R1. The best monitoring model integrated vegetation spectral indices, C-SAR and Nav data at V10 with an adjusted R2 of 0.75 achieved during calibration in the northern Pampa. During validation, it predicted NNI with an RMSE of 0.14 and a MAPE of 12% in the southeastern Pampa. The red-edge spectrum and Local Incidence Angle of C-SAR were necessary to monitor the corn N status via prediction of NNI. Thus, this study provided empirical models to remotely sensed corn N status within fields during vegetative period, serving as a foundational data for guiding future N management. [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]

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]

Abnormal ear development in corn (Zea mays L.) has been reported for more than 100 years. More recently, in 2016, widespread abnormal multiple ears per stalk node (herein termed as multi‐ears), barbell ears, and short husks were reported in cornfields located in the western and central Corn Belt (Illinois, Iowa, Nebraska, and Kansas), Eastern Colorado, and the Texas Panhandle region in the United States. Little was known about the underlying causes of these abnormalities. A literature review examining conditions potentially affecting corn ear formation, yield, and abnormal ears was conducted. Several abnormal ear symptoms appear to be formed by stress conditions such as extreme weather, limited solar radiation, and responses to plant growth regulators. The accumulation of these effects can result in the abortion of primary ears and the development of secondary abnormal ears, which has been a hypothesis for the last 15 years. Whether or not primary ear abortion is one of the factors for abnormal ears remains a valid question. Abnormal ears can be understood as the result of an "expression triangle": susceptible genetics, conducive environmental conditions, and unfavorable management practices. Together, these factors can interact and cause abnormal ears and lower yields. Active knowledge gaps include the environmental and physiological pathways to abnormal ears, their impact on grain quality and yield, their effect on other processes such as dry‐down and harvest ease, and an in‐depth understanding of differing genetics, environment, and management. Core Ideas: Extreme weather, low solar radiation, and growth regulators can be some of the causes.Primary ear abortion has been observed to correlate with the occurrence of abnormal ears.Factors affecting corn ear formation and abnormal ears result in lower yields.Genetic × environment × management interactions affect ear formation, yield, and abnormal ears. [ABSTRACT FROM AUTHOR]

Biotechnology has emerged as a valuable tool in the development of maize (Zea mays L.) hybrids with enhanced nitrogen (N) use efficiency. Recent work has described the positive effects of an increased and extended expression of the zmm28 transcription factor (Event DP202216) on maize yield productivity. In this study, we expand on the previous findings studying maize N uptake and utilization in DP202216 transgenic hybrids compared to wild-type (WT) controls. Isotope 15N labeling demonstrates that DP202216 hybrids have an improved N uptake during late-vegetative stages (inducing N storage in lower leaves of the canopy) and, thus, N uptake efficiency (N uptake to applied N ratio) relative to WT. Through both greater N harvest index and reproductive N remobilization, transgenic plants were able to achieve better N utilization efficiency (yield to N uptake ratio). Our findings suggest the DP202216 trait could open new avenues for improving N uptake and utilization efficiencies in maize. Isotope 15N labeling demonstrates that zmm28 transgenic maize have an improved N uptake during late-vegetative stages inducing N storage in lower leaves of the canopy and, thus, N uptake efficiency relative to wild-type maize. [ABSTRACT FROM AUTHOR]

Over the past century of maize (Zea mays L.) breeding, grain yield progress has been the result of improvements in several other intrinsic physiological and morphological traits. In this study, we describe (i) the contribution of kernel weight (KW) to yield genetic gain across multiple agronomic settings and breeding programs, and (ii) the physiological bases for improvements in KW for US hybrids. A global-scale literature review concludes that rates of KW improvement in US hybrids were similar to those of other commercial breeding programs but extended over a longer period of time. There is room for a continued increase of kernel size in maize for most of the genetic materials analysed, but the trade-off between kernel number and KW poses a challenge for future yield progress. Through phenotypic characterization of Pioneer Hi-Bred ERA hybrids in the USA, we determine that improvements in KW have been predominantly related to an extended kernel-filling duration. Likewise, crop improvement has conferred on modern hybrids greater KW plasticity, expressed as a better ability to respond to changes in assimilate availability. Our analysis of past trends and current state of development helps to identify candidate targets for future improvements in maize. [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]

Corn (Zea mays L.)–soybean [Glycine max (L.) Merr.] is among the most typical crop rotations in the U.S. Corn Belt, and N is the most limiting nutrient for both crops. This study aims to assess the effects of N management for corn on the following soybean crop. Two corn–soybean rotation N fertilizer rate studies—a long‐term study (1983–2020, Case Study I) and a two‐season study (2019–2020, Case Study II)—were conducted in Kansas (United States). Case Study I focused on soybean seed yield as the response variable, whereas Case Study II included a detailed seasonal characterization of soil N, symbiotic N fixation (SNF), and plant N uptake for soybean considering N fertilizer rates on the previous corn crop. Apparent N budgets from corn (N fertilizer minus grain N removal) ranged from approximately −100 to approximately +50 kg N ha−1, and soybean yields were slightly or not affected by corn N management. Case Study I showed that long‐term N budgets in corn crops did not affect the following soybean crop yields. In Case Study II, the previous corn N management produced negative or small N surplus that influenced neither soil residual N nor SNF, without compromising soybean productivity. Farmers applying close to economic optimum N rates on corn will likely not generate scenarios of N surplus to compromise SNF or soybean yields. Forthcoming research should further address how long‐term and large soil N mining or surplus in corn may enhance or inhibit N fixation for the next soybean crop. Core Ideas: Soybean yields were slightly or not affected by N management of the previous corn crop.Soil N during soybean season was not affected by previous corn N management.Apparent N budgets from previous corn were mostly below zero or slightly positive.Small N surplus (<50 kg N ha−1) did not influence soybean yields and N fixation.Long‐term N budgets in corn crops did not affect next‐season soybean yields. [ABSTRACT FROM AUTHOR]

Intensive study for more than 100 yr has resulted in a good understanding of corn's (Zea mays L.) growth and development. However, abnormal development of ears in corn was reported in several U.S. states, including Texas, Colorado, Kansas, Nebraska, Iowa, and Illinois, during 2016, stretching our understanding. A comprehensive review of the literature was conducted to identify abnormal ears' symptoms, causes, and timing of development. This study aimed to (a) describe and summarize previously reported ear symptoms, (b) document recent widespread symptoms of major concern, and (c) describe our current understanding of the potential cause(s) and expected development timing for abnormal ears. In total, 10 previously reported symptoms of corn ears were found, including tassel, fasciated, arrested, pinched, blunt, silk‐balled, incomplete kernel set, banana‐shaped, zipper, and tipped‐back. Three additional recent widespread symptoms of major concern associated with significant yield reduction across a wide area in 2016 were described: multi‐ears, barbell‐ears, and short‐husk ears. The information available on several of the symptoms was limited, and the specific causes were unknown, highlighting the need for more research in this area. Despite this and based on existing knowledge, possible causal factors and postulated development timing (i.e., when the stress may have occurred) are presented for all symptoms. Abnormal ear development can be seen as the response to complex interactions among genetics, environment, and management practices. Ear abnormalities are detrimental to grain yield and quality, and their mitigation is imperative to efficient corn systems, crop resiliency, and sustainability. Core Ideas: Abnormal ears are a likely response to G × E × M interactions.Ten previously reported abnormality symptoms are discussed.Three recent widespread symptoms of major concern associated with lower yields are included.Abnormal ears can reduce grain yield and quality.The understanding and mitigation of abnormal ears are imperative for sustainable agriculture. [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]

Background: The fraction of intercepted photosynthetically active radiation (fPARi) is typically described with a non-linear function of leaf area index (LAI) and k, the light extinction coefficient. The parameter k is used to make statistical inference, as an input into crop models, and for phenotyping. It may be estimated using a variety of statistical techniques that differ in assumptions, which ultimately influences the numerical value k and associated uncertainty estimates. A systematic search of peer-reviewed publications for maize (Zea Mays L.) revealed: (i) incompleteness in reported estimation techniques; and (ii) that most studies relied on dated techniques with unrealistic assumptions, such as log-transformed linear models (LogTLM) or normally distributed data. These findings suggest that knowledge of the variety and trade-offs among statistical estimation techniques is lacking, which hinders the use of modern approaches such as Bayesian estimation (BE) and techniques with appropriate assumptions, e.g. assuming beta-distributed data. Results: The parameter k was estimated for seven maize genotypes with five different methods: least squares estimation (LSE), LogTLM, maximum likelihood estimation (MLE) assuming normal distribution, MLE assuming beta distribution, and BE assuming beta distribution. Methods were compared according to the appropriateness for statistical inference, point estimates' properties, and predictive performance. LogTLM produced the worst predictions for fPARi, whereas both LSE and MLE with normal distribution yielded unrealistic predictions (i.e. fPARi < 0 or > 1) and the greatest coefficients for k. Models with beta-distributed fPARi (either MLE or Bayesian) were recommended to obtain point estimates. Conclusion: Each estimation technique has underlying assumptions which may yield different estimates of k and change inference, like the magnitude and rankings among genotypes. Thus, for reproducibility, researchers must fully report the statistical model, assumptions, and estimation technique. LogTLMs are most frequently implemented, but should be avoided to estimate k. Modeling fPARi with a beta distribution was an absent practice in the literature but is recommended, applying either MLE or BE. This workflow and technique comparison can be applied to other plant canopy models, such as the vertical distribution of nitrogen, carbohydrates, photosynthesis, etc. Users should select the method balancing benefits and tradeoffs matching the purpose of the study. [ABSTRACT FROM AUTHOR]

Genomic prediction provides an efficient alternative to conventional phenotypic selection for developing improved cultivars with desirable characteristics. New and improved methods to genomic prediction are continually being developed that attempt to deal with the integration of data types beyond genomic information. Modern automated weather systems offer the opportunity to capture continuous data on a range of environmental parameters at specific field locations. In principle, this information could characterize training and target environments and enhance predictive ability by incorporating weather characteristics as part of the genotype-by-environment (G×E) interaction component in prediction models. We assessed the usefulness of including weather data variables in genomic prediction models using a naïve environmental kinship model across 30 environments comprising the Genomes to Fields (G2F) initiative in 2014 and 2015. Specifically four different prediction scenarios were evaluated (i) tested genotypes in observed environments; (ii) untested genotypes in observed environments; (iii) tested genotypes in unobserved environments; and (iv) untested genotypes in unobserved environments. A set of 1,481 unique hybrids were evaluated for grain yield. Evaluations were conducted using five different models including main effect of environments; general combining ability (GCA) effects of the maternal and paternal parents modeled using the genomic relationship matrix; specific combining ability (SCA) effects between maternal and paternal parents; interactions between genetic (GCA and SCA) effects and environmental effects; and finally interactions between the genetics effects and environmental covariates. Incorporation of the genotype-by-environment interaction term improved predictive ability across all scenarios. However, predictive ability was not improved through inclusion of naive environmental covariates in G×E models. More research should be conducted to link the observed weather conditions with important physiological aspects in plant development to improve predictive ability through the inclusion of weather data. [ABSTRACT FROM AUTHOR]

During a century of maize (Zea mays L.) breeding, yield genetic gain was largely determined by increased reproductive resilience under stress and establishment of sink size (number of grains per unit area). Considering grains as competing sinks for C and N assimilates, understanding changes in the C and N economy can provide insights to define selection criteria towards a sustained yield improvement. A cognitive framework to define such criteria may consist in connecting the water‐soluble carbohydrates (WSC) and N dynamics in stem and leaves with the reproductive sink strength during post‐flowering. The objectives of this study were to advance such framework by (a) quantifying grain N demand and remobilization capacity in two hybrids as affected by N availability, and (b) formalizing how the interplay between N and WSC remobilization influence grain growth. Single cross hybrids 3394 and P1197 (released in 1991 and 2014) were evaluated to represent keystone phases of germplasm development (conventional and molecular breeding eras). P1197 outyielded 3394 consistently under high N supply, and its better N utilization efficiency was reflected through a lower grain N concentration. Under high N, the ability to maintain a greater leaf area during late grain‐filling for P1197 resulted in a reduced leaf N remobilization. Although yield was not limited by C supply, 3394 exposed greater remobilization of WSC during late grain‐filling. This study contributes to advance the development of a relevant C to N framework to further analyze drivers of genetic yield gain and assist in selection strategies in maize. [ABSTRACT FROM AUTHOR]

Yield estimations are of great interest to support interventions from governmental policies and to increase global food security. This study presents a novel model to perform in‐season corn yield predictions at the US‐county level, providing robust results under different weather and yield levels. The objectives of this study were to: (i) evaluate the performance of a random forest classification to identify corn fields using Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and weather variables (temperature, precipitation, and vapor pressure deficit, VPD); (ii) evaluate the contribution of weather variables when forecasting corn yield via remote sensing data, and perform a sensitivity analysis to explore the model performance in different dates; and (iii) develop a model pipeline for performing in‐season corn yield predictions at county‐scale. Main outcomes from this study were: (i) high accuracy (87% on average) for corn field classification achieved in late August, (ii) corn yield forecasts with a mean absolute error (MAE) of 0.89 Mg ha−1, (iii) weather variables (VPD and temperature) highly influenced the model performance, and (iv) model performance decreased when predictions were performed early in the season (mid‐July), with MAE increasing from 0.87–1.36 Mg ha−1 when forecast timing changed from day of year 232–192. This research portrays the benefits of integrating statistical techniques and remote sensing to field survey data in order to perform more reliable in‐season corn yield forecasts. [ABSTRACT FROM AUTHOR]

Objectives: Advanced tools and resources are needed to efficiently and sustainably produce food for an increasing world population in the context of variable environmental conditions. The maize genomes to fields (G2F) initiative is a multi-institutional initiative effort that seeks to approach this challenge by developing a flexible and distributed infrastructure addressing emerging problems. G2F has generated large-scale phenotypic, genotypic, and environmental datasets using publicly available inbred lines and hybrids evaluated through a network of collaborators that are part of the G2F's genotype-by-environment (G × E) project. This report covers the public release of datasets for 2014–2017. Data description: Datasets include inbred genotypic information; phenotypic, climatic, and soil measurements and metadata information for each testing location across years. For a subset of inbreds in 2014 and 2015, yield component phenotypes were quantified by image analysis. Data released are accompanied by README descriptions. For genotypic and phenotypic data, both raw data and a version without outliers are reported. For climatic data, a version calibrated to the nearest airport weather station and a version without outliers are reported. The 2014 and 2015 datasets are updated versions from the previously released files [1] while 2016 and 2017 datasets are newly available to the public. [ABSTRACT FROM AUTHOR]

Plant breeders face the challenge of genotype × environment interaction (G × E) in comprehensively breeding for expanded geographic regions. An improved understanding of G × E sensitivity of traits and the environmental features that effectively discriminate among genotypes will enable more efficient breeding efforts. In this study of 31 maize (Zea mays L.) inbreds grown in 36 environments that are part of the Genomes to Fields Initiative, we measured 14 traits, including flowering date, height, and yield components (i.e., ear and kernel dimensions) to (i) identify traits that are the most sensitive indicators of G × E; (ii) determine how geographic location and weather factors influence environments' discriminability of inbreds; and (iii) detect patterns of stability in better and worse discriminating environments. Genotype × environment interaction explained between 9.0–20.4% of the phenotypic variance with greater effects in the yield‐component traits. Discriminability of environments varied by trait. Midwest locations (where 26 of the 31 inbreds were developed) were among the most discriminating environments for more traits, while environments in the West and East tended to be less discriminating. Weather factors during silking were significantly different between the most and least discriminating environments more often than average weather across the season or during the period from planting to silking. Stability of genotypes varied by trait, and performance was usually not correlated with stability. The dissection of complex traits, such as yield into component traits, appears to be a useful approach to understand how environmental factors differentially affect phenotype. [ABSTRACT FROM AUTHOR]

Increasing crop intensification while enhancing soil conservation is an important aspect of global food security. Intercropping (syn. interseeding) between cash crops and tropical forages improves soil cover, prevents soil erosion, and enhances land use via crop and livestock integration. In Brazil, intercropping between maize (Zea mays L.) and Brachiaria grass (Urochloa spp.) is the most commonly adopted system, with opportunities for expansion across tropical and subtropical landscapes worldwide. Nonetheless, there is little consensus on the effects of intercropping on the performance of each individual crop species. A meta-analysis was conducted to quantify the effect of intercropping on maize grain yield and Brachiaria grass biomass production and to understand the impact of environmental and management factors on intercropping performance. A literature search was conducted to retrieve peer-reviewed studies carried out on this intercropping system under field conditions. Manuscripts were scrutinized and included in this meta-analysis when they met the following criteria: i) data on maize grain yield and Brachiaria grass biomass under intercropping were reported, ii) inclusion of a control treatment (monocropping) for at least one species, and iii) field trials with at least three randomized replications. Our database included 429 data points extracted from 56 published manuscripts from all regions of Brazil. Relative to monocropping, intercropping reduced maize grain yield (−5.6 %) and Brachiaria grass biomass production (−64 %) in all scenarios. The intercropped maize grain yield was less affected under the following conditions: i) in the subtropical climate zone (−3.6 %), ii) sowing the intercrop early in the season (−5.8 %), iii) applying monocot-selective postemergence herbicides (−4.4 %), iv) relay temporal arrangement (−2.8 %), and v) with N and P fertilization rates above 150 and 35 kg ha-1, respectively. In relative terms, Brachiaria grass performance was more affected by i) intercropping adoption, with a reduced biomass penalty under a tropical climate (−63.9 %), ii) late sowing time for the intercrop (−59.5 %), iii) application of N fertilizer at a rate above 50 kg ha-1 (−70.5 %), and iv) non-use of postemergence herbicides (−21.9 %). Maize-Brachiaria grass intercropping was an effective alternative to produce residue (no-till system adoption) or forage (crop-livestock integrated system adoption) with reduced maize grain yield penalties, diversifying cropping systems in Brazil. Despite the production penalties for both crop species, considering different relative values, specific management practices and climate conditions could minimize yield penalties. Scenarios favoring maize yields may be preferred for intercropping systems focused on improving soil quality; in contrast, Brachiaria grass biomass may benefit the integration of crop-livestock systems focused on ensuring forage production. [Display omitted] • Intercropping decreased the productivity of maize by 5.6 % and of Brachiaria by 64 %. • The yield penalty varied across agronomic and environmental factors. • Sowing time and climate were key factors defining the performance of intercropping systems. • Intercropping was effective for the establishment of no-tillage systems. • Maize-Brachiaria grass intercropping is a way to implement crop–livestock systems. [ABSTRACT FROM AUTHOR]

Corn (Zea mays L.) is one of the most sensitive crops to planting pattern and early-season uniformity. The most common method to determine number of plants is by visual inspection on the ground but this field activity becomes time-consuming, labor-intensive, biased, and may lead to less profitable decisions by farmers. The objective of this study was to develop a reliable, timely, and unbiased method for counting corn plants based on ultra-high-resolution imagery acquired from unmanned aerial systems (UAS) to automatically scout fields and applied to real field conditions. A ground sampling distance of 2.4 mm was targeted to extract information at a plant-level basis. First, an excess greenness (ExG) index was used to individualized green pixels from the background, then rows and inter-row contours were identified and extracted. A scalable training procedure was implemented using geometric descriptors as inputs of the classifier. Second, a decision tree was implemented and tested using two training modes in each site to expose the workflow to different ground conditions at the time of the aerial data acquisition. Differences in performance were due to training modes and spatial resolutions in the two sites. For an object classification task, an overall accuracy of 0.96, based on the proportion of corrected assessment of corn and non-corn objects, was obtained for local (per-site) classification, and an accuracy of 0.93 was obtained for the combined training modes. For successful model implementation, plants should have between two to three leaves when images are collected (avoiding overlapping between plants). Best workflow performance was reached at 2.4 mm resolution corresponding to 10 m of altitude (lower altitude); higher altitudes were gradually penalized. The latter was coincident with the larger number of detected green objects in the images and the effectiveness of geometry as descriptor for corn plant detection. [ABSTRACT FROM AUTHOR]

For maize (Zea mays L.), early planting date could be of advantage to high yields but a review of planting date effect on high-yielding data is not yet available. Following this rationale, a synthesis-analysis was conducted from the farmer annual maize contest-winner data (n = 16171 data points; 2011–2016 period); cordially provided by the National Corn Growers Association and a scientific literature dataset collected from research publications since the last three decades. The main objectives of this study were to: (i) identify spatial yield variability within the high-yielding maize dataset; (ii) understand the impacts of planting date on yield variability; (iii) explore the effect of management practices on maize yield-planting date relationship, and (iv) utilize the yield-planting date dataset collected via farmer contest-winner as a benchmarking data to be compared to the compendium of scientific literature available for yield-planting date relationship for the primary US maize producing regions. Major findings of this study are: (i) significant correlation between planting date and latitude, (ii) maize yield was maximized when planting window was 89–106 day of the year (DOY) for the 30–35°N, 107–118 DOY for the 35–40°N, <119 DOY for 40–45°N, and <129 DOY for 45–50°N, and (iii) both, yield contest and literature datasets portrayed that planting date becomes a more relevant factor when planting late, presenting a relatively smaller planting window in high-compared to low-latitudes. [ABSTRACT FROM AUTHOR]

The potential benefit of drought-tolerant (DT) corn (Zea mays L.) hybrids may depend on drought intensity, duration, crop growth stage (timing), and the array of drought tolerance mechanisms present in selected hybrids. We hypothesized that corn hybrids containing DT traits would produce more consistent yields compared to non-DT hybrids in the presence of drought stress. The objective of this study was to define types of production environments where DT hybrids have a yield advantage compared to non-DT hybrids. Drought tolerant and non-DT hybrid pairs of similar maturity were planted in six site-years with different soil types, seasonal evapotranspiration (ET), and vapor pressure deficit (VPD), representing a range of macro-environments. Irrigation regimes and seeding rates were used to create several micro-environments within each macro-environment. Hybrid response to the range of macro and micro-environmental stresses were characterized in terms of water use efficiency, grain yield, and environmental index. Yield advantage of DT hybrids was positively correlated with environment ET and VPD. Drought tolerant hybrids yielded 5 to 7% more than non-DT hybrids in high and medium ET environments (>430 mm ET), corresponding to seasonal VPD greater than 1200 Pa. Environmental index analysis confirmed that DT hybrids were superior in stressful environments. Yield advantage for DT hybrids appeared as yield dropped below 10.8 Mg ha-1 and averaged as much as 0.6-1 Mg ha-1 at the low yield range. Hybrids with DT technology can offer a degree of buffering against drought stress by minimizing yield reduction, but also maintaining a comparable yield potential in high yielding environments. Further studies should focus on the physiological mechanisms presented in the commercially available corn drought tolerant hybrids. [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]

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]

For farmers, policymakers, and government agencies, it is critical to accurately define agricultural crop phenology and its spatial-temporal variability. At the moment, two approaches are utilized to report crop phenology. On one hand, land surface phenology provides information about the overall trend, whereas weekly reports from USDA-NASS provide information about the development of particular crops at the regional level. High-cadence earth observations might help to improve the accuracy of these estimations and bring more precise crop phenology classifications closer to what farmers demand. The second component of the proposed solution requires the use of robust classifiers (e.g., random forest, RF) capable of successfully managing large data sets. To evaluate this solution, this study compared the output of a RF classifier model using weather, two different satellite sources (Planet Fusion; PF and Sentinel-2; S-2), and ground truth data to improve maize (Zea mays L.) crop phenology classification using two regions of Kansas (Southwest and Central) as a testbed during the 2017 growing season. Our findings suggests that high temporal resolution (PF) data can significantly improve crop classification metrics (f1-score = 0.94) relative to S-2 (f1-score = 0.86). Additionally, a decline in the f1-score between 0.74 and 0.60 was obtained when we assessed the ability of S-2 to extend the temporal forecast for crop phenology. This research highlights the critical nature of very high temporal resolution (daily) earth observation data for crop monitoring and decision making in agriculture. [ABSTRACT FROM AUTHOR]

Concomitantly pursuing superior maize (Zeamays L.) productivity with grain quality is essential for food security. Therefore, this study provides a meta-analysis of 21 studies assembled from the scientific literature to tackle the effect of the two most limiting factors for maize production, water and nitrogen (N), and their impacts on grain quality composition, herein focused on protein, oil, and starch concentrations. Water stress levels resulted in erratic responses both in direction and magnitude on all the grain quality components, plausibly linked to a different duration, timing, and intensity of water stress treatments. Nitrogen fertilization more consistently affected the grain protein concentration, with a larger effect size for protein as fertilizer N levels increased (protein change of +14% for low, ≤70 kg N ha−1; +21% for medium, >70–150 kg N ha−1; and +24% for high, >150 kg N ha−1). Both starch and oil grain concentrations presented less variation to fertilizer N levels. The positive protein–oil correlation (r = 0.49) permitted to infer that although the oil concentration may reach a plateau (8%), further increases in protein are still possible. Augmented research on grain quality is warranted to sustain food production but with both high nutritional and energetic value for the global demand. [ABSTRACT FROM AUTHOR]

• Bayesian analysis shows little variability of critical N curve for maize. • The uncertainty of the critical plant N concentration is larger with lower biomass. • Modifications in N dilution across genotypes could be linked to canopy architecture. Improving the prediction of plant N status for field crops require robust estimation of their critical N dilution curves and their parameters. For maize (Zea mays L.), several studies have contributed to develop separate critical N curves for different genotype, environment, and management (GxExM) combinations, but so far no rigorous analysis has been formally conducted to assess if the use of different curves is fully supported by experimental data and to understand if a more universal curve could be used for defining the plant N status of this crop. This study presents a global synthesis of maize data related to plant biomass and plant N concentration (%N) with the main objective of implementing a Bayesian framework for fitting critical N curves (Nc = A 1 W−A 2) and testing the existence of differences in the main parameters of the curve (A 1 and A 2) across varying GxExM combinations. Five publications including trials with at least four sampling times for plant biomass were selected based on a systematic review of the literature. Biomass and %N pairs (n = 369) measured during vegetative growth were extracted from the selected papers and included in a dataset covering four countries (China, US, Canada, and France) and twenty different GxExM combinations. Results of the Bayesian analysis revealed that the critical plant %N and biomass fitted for each GxExM combination presented negligible statistical differences for A 1 but minor variation for A 2 reflecting changes in N dilution pattern with biomass mostly when W was above 5 Mg ha-1 (%Nc<2%). A hypothesis of changes in plant canopy architecture, related to variations in GxM (e.g., plant density, row spacing, hybrids) related to variations in the N dilution curve was postulated and needs further evaluation. Refining the estimation of %Nc will improve the assessment of plant N status. [ABSTRACT FROM AUTHOR]

• 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]

• Intercropping did not affect N cycling and N fertilizer recovery from planting to grain harvest. • Greater N cycling by palisadegrass from intercrop patterns did not necessarily result in greater N fertilizer recovery after grain harvest. • Intercrop planted during conventional maize planting season led to greater N cycling. • Intercrop serving as cover crop or pasture attained a comparable N content, but nutrient removal via simulated grazing resulted in a greater negative N budget. In tropical agriculture, maize (Zea mays L.) intercropped with palisadegrass (Brachiaria brizantha cv. Marandu) is considered a highly relevant cropping system to deliver both residue and forage. However, the benefits of inclusion of palisadegrass in maize systems and its influence on fertilizer dynamics and nitrogen (N) budget as well as the period after maize harvest for which palisadegrass continues to grow remain unclear. Thus, we conducted field studies to determine crop growth, end-season biomass, N fertilizer recovery, and overall N budget between an intercrop and a monoculture at two different sites during conventional and late-planted maize seasons. The cropping systems evaluated were maize and palisadegrass in monocultures and two intercrop patterns, serving as cover crop or forage with simulated grazing. From planting to grain harvest, intercrop patterns did not affect grain yield, final N content, and N fertilizer dynamics. Post-harvest, palisadegrass from intercrop patterns improved biomass from 1 to 10 Mg ha−1 and N content from 8 to 300 kg ha−1, relative to fallow after maize monoculture. Contribution of palisadegrass intercropping on N fertilizer recovery was low at approximately 4.5 kg ha-1 when both the crops were growing simultaneously and approximately 2.4 kg ha−1 following maize grain harvest. In three out of four sites over 2 years, intercrop patterns did not affect total N fertilizer recovery compared with their monocultures, averaging at approximately 51 %. Advantages of maize-palisadegrass intercropping on biomass, yield, and N cycling were documented at the site where maize was planted during the conventional season, despite the yearly variation in weather conditions. A negative N budget of −230 kg ha−1 was estimated when grazing was simulated during the conventional maize planting season, suggesting greater N requirements of the intercropped palisadegrass when serving as forage. [ABSTRACT FROM AUTHOR]

Land use maps specifying up-to-date acreage information on maize (Zea mays L.) cropping patterns are required by many stakeholders in Vietnam. Government statistics, however, lag behind by one year, and the official land use maps are only updated at 5-year intervals. The aim of this study was to apply the Savitzky–Golay algorithm to reconstruct noisy Enhanced Vegetation Index (EVI) time series (2003–2018) from Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MOD13Q1) to allow timely detection of changes in maize crop phenology, and then to employ a linear kernel Support Vector Machine (SVM) classifier on the reconstructed EVI time series to prepare the present-day maize cropping pattern map of Dak Lak province of Vietnam. The method was able to specify the spatial extent of areas cropped to maize with an overall map accuracy of 79% and could also differentiate the areas cropped to maize just once versus twice annually. The by-district mapped maize acreage shows a good agreement with the official governmental data, with a 0.93 correlation coefficient (r) and a root mean square deviation (RMSD) of 1624 ha. [ABSTRACT FROM AUTHOR]

Objectives: Crop improvement relies on analysis of phenotypic, genotypic, and environmental data. Given large, well-integrated, multi-year datasets, diverse queries can be made: Which lines perform best in hot, dry environments? Which alleles of specific genes are required for optimal performance in each environment? Such datasets also can be leveraged to predict cultivar performance, even in uncharacterized environments. The maize Genomes to Fields (G2F) Initiative is a multi-institutional organization of scientists working to generate and analyze such datasets from existing, publicly available inbred lines and hybrids. G2F's genotype by environment project has released 2014 and 2015 datasets to the public, with 2016 and 2017 collected and soon to be made available. Data description: Datasets include DNA sequences; traditional phenotype descriptions, as well as detailed ear, cob, and kernel phenotypes quantified by image analysis; weather station measurements; and soil characterizations by site. Data are released as comma separated value spreadsheets accompanied by extensive README text descriptions. For genotypic and phenotypic data, both raw data and a version with outliers removed are reported. For weather data, two versions are reported: a full dataset calibrated against nearby National Weather Service sites and a second calibrated set with outliers and apparent artifacts removed. [ABSTRACT FROM AUTHOR]

• Data fusion offered new opportunities for agricultural monitoring. • Spatial data patterns affected model performance in regions with high variability. • Aggregating crop year data presented high accuracy in transfer learning. • Early crop classification resulted in similar overall accuracy to full length time-series. • Crop classification and mapping were produced for Rio Grande do Sul state, Brazil. Field-scale crop monitoring is essential for agricultural management and policy making for food security and sustainability. Automating crop classification process while elaborating a workflow is a key step for reliable and precise crop mapping. This study aims to develop an approach for crop classification in the state of Rio Grande do Sul, Brazil, following the specific goals of i) evaluating spatial satellite-based features to guide crop data collection; ii) testing transfer learning model with subsequent growing season data; iii) examining accuracy in early-season prediction model; and lastly, iv) developing a crop classification model for estimating large scale crop area. As main data inputs, Sentinel-2, Sentinel-1, and Shuttle Radar Topographic Mission (SRTM) Digital Elevation data were used to extract features to input in the Random Forest classifier. Spatial variability of satellite features was evaluated using Moran's I Index and cluster k-means. Crop area prediction data were obtained at municipality level to compare with census data (standard method). A crop summer map layer was generated for three major crops: soybeans (Glycine max L.), corn (Zea mays L.), and rice (Oryza sativa L.) in the state of Rio Grande do Sul, Brazil. The crop classification model achieved an overall accuracy of 0.95. Model performance was influenced by sample size and spatial variability of the samples. The random forest model was transferred to the next growing season with 0.89 and 0.91 overall accuracy for 250 and 750 samples, respectively. However, overall accuracy increased from 0.93 to 0.95 when 50 to 250 samples of same-year data was aggregated to the model. Similar accuracy was obtained for predictions done with data until March relative to when the entire season was considered, until May. When data for more growing seasons were aggregated, the model produced more accurate early season predictions (January and February). Soybean prediction area obtained the highest performance (R2 = 0.94), relative to rice (R2 = 0.90) and corn (R2 = 0.37). The rice prediction area presented a high precision, but the crop area was overestimated due to errors with wetland target relative to other class. Lastly, this study presents the first crop map layer of the three major field crops for the state of Rio Grande do Sul, Brazil, serving as a foundation for the creation of crop type maps for other states in the country and around the globe. [ABSTRACT FROM AUTHOR]

The photoassimilates and nitrogen (N) allocation in grain crops can be studied via the harvest index (HI) and the N harvest index (NHI). These two variables should be considered in breeding programs seeking more effective use of resources in maize (Zea mays L.) and other grain crops. Molecular biotechnology has emerged as a complement to improve not only maize grain yield but also resource utilization efficiency. A recent study reported a beneficial impact of increasing and extending the expression of a maize MADS-box transcription factor (zmm28) on the NHI. This research aimed to further identify enhancements of C-N partitioning (HI, NHI) in DP202216 maize hybrids recognizing the main physiological and morphological traits associated to the C-N dynamics via in-season intra-canopy measurements. Two DP202216 elite hybrids were evaluated with their respective wild-type (WT) controls during two field growing seasons under two N regimes. The N nutrition index (NNI), as crop N status, was quantified to avoid misinterpretations of the results. The DP202216 trait showed, on average, a ∼6% increase in the HI with a concomitant positive impact of ∼9% in the NHI across hybrids, N regimes, and seasons. Those increases were consistent at different crop NNI. After accounting for the NNI, it was shown that the increases in both HI and NHI were supported by the accumulation of more N in leaves (mainly in both lower and middle sections of the canopy vertical profile) and more water-soluble carbohydrates (WSC) in stems before flowering, combined with more aggressive post-anthesis remobilization. In this study, it was shown an increase in the efficient use of crop resources in DP202216 hybrids with respect to their isogenic WT controls at similar NNI via increments in C and N partitioning (HI and NHI). This physiological and morphological understanding of C- and N-related traits addressed the challenge of optimizing the effective use of resources in maize breeding programs. To better understand traits by management interactions with yield and yield stability, DP202216 hybrid evaluations across a broad range of commercially relevant N scenarios would be valuable. • Zmm28 hybrids increased both the harvest index (HI) and nitrogen HI (NHI). • Zmm28 hybrids displayed more leaf N and stem water-soluble carbohydrates (WSC) accumulation. • The increase in HI and NHI was supported by greater WSC (stem) and N (leaf and stem) remobilization. • This study provides knowledge for optimizing the effective use of resources in grain crops. [ABSTRACT FROM AUTHOR]

In the temperate-humid region of Argentina, nitrogen (N) fertilisers in maize (Zea mays L.) are mainly applied around sowing, whereas N-splitting is rare and occurs during early vegetative stages. Splitting and late N fertilisation, even up to silking (R 1), effects on yield have been recently studied. However, to the extent of our knowledge, these studies have mainly focused on the effect of these strategies on maize yield, but less on the crop N economy. The aims were to study the mechanisms explaining the effect of i) splitting N and ii) additional N application at R 1 on yield, post-flowering N uptake, kernel weight, N uptake efficiency, and the relationship of these variables with the N nutrition index (NNI). Seven experiments were carried out throughout the humid temperate region of Argentina, evaluating seven treatments: a control without N fertilisation, N rate entirely applied at sowing or split between sowing, eight leaf (V 8), and R 1 , an additional N rate at R 1 , and an N sufficiency treatment. The scenario with split N application had no adverse effect on either yield or N uptake at maturity (P > 0.05), with less pre-flowering N uptake compensated with more post-flowering N uptake in late N applications. In turn, increases in both yield and kernel weight were related to increases in post-flowering N uptake. Positive yield responses to N-splitting (plateauing at 450 kg ha-1) were obtained with yield responses to N greater than 3107 kg ha-1. Likewise, yield responses to additional N at R 1 (plateauing at 1107 kg ha-1) were evident with yield responses greater than 2943 kg ha-1. The NNI at R 1 accounted for variations in post-flowering N uptake and yield, as well as yield responses to additional N at R 1 and their impact on kernel weight. Results show that N-splitting and late N fertilisation are promising strategies that, combined with crop N status monitoring, could lead to improvements in both maize yield and N economy. • Split and late N fertilisations were evaluated for maize in the humid-temperate region of Argentina. • Grain yield (GY) responses to N-splitting and to additional N at R 1 were recorded. • GY responses to N-splitting and to additional N at R 1 were higher for the most responsive sites. • N-splitting did not reduce GY and increased N uptake efficiency. • GY response to additional N at R 1 was negatively associated with crop N status at R 1. [ABSTRACT FROM AUTHOR]

High-dimensional and high-throughput genomic, field performance, and environmental data are becoming increasingly available to crop breeding programs, and their integration can facilitate genomic prediction within and across environments and provide insights into the genetic architecture of complex traits and the nature of genotype-by-environment interactions. To partition trait variation into additive and dominance (main effect) genetic and corresponding genetic-by-environment variances, and to identify specific environmental factors that influence genotype-by-environment interactions, we curated and analyzed genotypic and phenotypic data on 1918 maize (Zea mays L.) hybrids and environmental data from 65 testing environments. For grain yield, dominance variance was similar in magnitude to additive variance, and genetic-by-environment variances were more important than genetic main effect variances. Models involving both additive and dominance relationships best fit the data and modeling unique genetic covariances among all environments provided the best characterization of the genotype-by-environment interaction patterns. Similarity of relative hybrid performance among environments was modeled as a function of underlying weather variables, permitting identification of weather covariates driving correlations of genetic effects across environments. The resulting models can be used for genomic prediction of mean hybrid performance across populations of environments tested or for environment-specific predictions. These results can also guide efforts to incorporate high-throughput environmental data into genomic prediction models and predict values in new environments characterized with the same environmental characteristics. [ABSTRACT FROM AUTHOR]

Region-specific guidelines for maize in the US Great Plains are needed to account for variability in farmer practices and inherent soil and climate differences. Therefore, it becomes critical to define environment tailored management strategies for maize crop to improve and sustain yields over time. The aims of this study were to: i) define homogenous maize yield environments classified as related to the maximum yield and its stability over time in the US central Great Plains region; ii) explore the combinations of planting date and hybrid maturity that lead to optimal yield for each of the previously defined homogenous maize yield environments; and iii) identify environments presenting different windows for maximizing yields (combinations of planting date by hybrid maturity). Hybrid maturity (growth cycle) by planting date combinations were explored at 70 sites within central and eastern Kansas and northern Oklahoma (herein defined as the US central Great Plains region) using 30 years of historical weather data. In addition to the analysis of an extensive maize field dataset, a crop growth model, Agricultural Production Systems Simulator (APSIM), was utilized to evaluate the crop yield and phenology of all hybrid maturity by planting date combinations to define environment tailored management strategies. The simulated yields were spatially clustered based on attainable yield and stability over time to delimit more homogeneous productivity environments. Overall, greater yields were attained with long-maturing hybrids (comparative relative maturity, CRM, 101 and 115). Delaying the planting date from April to mid-May decreased yield in eastern environments. Moreover, late planting maintained yields and increased stability in central environments. For central Kansas and Oklahoma, anticipation of physiological maturity date (3–16 days) with reduced yield penalties (<5%) were possible by using short-season hybrid maturities (CRM 85). Future scenarios for intensification could be explored in those environments presenting more windows for maximizing yields, either with early planting and harvest dates or via late planting providing time for a short crop before maize. Adoption of more region-specific guidelines for maize management could help to optimize yield and its stability over time and seek further opportunities to diversify risk and intensify our current maize-based farming systems in the US central Great Plains. • Higher yields were attained with long-maturing hybrids (CRM 101 and 115). • Delays in planting date after mid-May decreased yield in eastern environments. • Late planting maintained yields and increased stability in central environments (CE). • CRM 85 reached maturity earlier with similar yields compared to longer CRMs in CE. • Self-report on farm yield data supported the simulated yield trends. [ABSTRACT FROM AUTHOR]

• Remote-sensing based crop type maps and available datasets enable rotation mapping. • Continuous soybean was the most common crop rotation in Rio Grande do Sul, Brazil. • Crop rotation in lowlands showed an increase in soybean area in rotation with rice. • Crop rotation had more benefits for soybean yields according to different regions. • The study showed an end-to-end approach for crop rotation analyses at large-scale. Crop monitoring is a key process for agricultural management and policy making linked to food security and sustainability. The spatio-temporal assessment of crop-specific management is essential for mapping crop rotation at field-scale. The aims of this study were to generate a satellite-based data fusion approach for mapping crop rotation at field-scale and generating analyses of crop rotation patterns among different mesoregions, examining the impact of environmental factors on crop yields, within the Rio Grande do Sul, southern Brazil. Within this geographical region, soybeans (Glycine max (L.) Merr.), corn (Zea mays L.), and rice (Oryza sativa L.) are the major grain crops of the state. We have utilized a satellite-based data fusion crop type classification and mapping to extract spatial crop features during four growing seasons (2017–2021), associated to field boundary delineation to generate the crop rotation database. This database showed current crop rotation practices within the study region, highlighting continuous soybean (monocrop), and three soybeans with one corn year rotation as the most frequent in the state. Midwestern and Northwest mesoregions presented the highest mean municipality values of continuous crop with 76% and 61%, respectively. Crop rotation patterns in Porto Alegre metropolitan, Southwest, and Southern mesoregions showed soybean-rice rotation, but with a trend of increases in soybean area in detriment to rice in lowlands. Crop rotation effects on yield for soybeans varied from 20 up to 65% depending on the regions and years of crop rotation. Other crop rotation effects in the crop yields were not significant at the municipality level. Crop rotation showed more significant benefits for soybean yields in warm and wet climate, with higher bulk density and lower soil organic carbon. Lastly, this study presents the first crop rotation map layer for the entire state of Rio Grande do Sul, southern Brazil, serving as a foundation for the creation of similar database for other states in the country and around the globe. [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 intensification is a key aspect for achieving global food security and exploring options for optimizing land use and environmental resources. Following this rationale, a maize (Zea mays L.) – soybean (Glycine max L.) double cropping was tested to intensify current agricultural systems in the Southern region of the United States (US). The aims of this study were to i) assess the feasibility of cultivating maize-soybean double crop in the Southern US, ii) explore the best scenario of maize-soybean crops integrating field data with APSIM crop growth model via calculation of the land equivalent ratio (LER), and iii) evaluate the risk of crop failure for each crop combination. For this case study, one year field research and in-silico experiments using 30-years of weather data were analyzed to evaluate the different lengths of growth cycle for both crops for yield, LER, and percentage of success. The feasibility of the maize-soybean sequence was determined by increasing yield gain for the soybean crop while maintaining similar yield for maize crop. Simulated LER values ranged from 1.1 to 1.3 with the maize hybrid 110 comparative relative maturity followed by a soybean maturity group 3.0 as the best combination for maximum LER. Lastly, long-term simulations confirmed the low risk associated with maize-soybean double crop sequences across sowing dates. Expanding the implementation of this farming system could produce additional benefits not only on productivity and land use efficiency but in overall economic return for farmers in the Southern US region. [Display omitted] • Maize-soybean double cropping is a feasible production system in the Southern US. • Maximum land equivalent ratio was achieved with long-season maize with intermediate to long soybean maturity group. • Maize yield contribution to LER is a key factor for the overall maize-soybean system. • Soybean risk of not reaching maturity across sowing dates and maturities was low (<25%). [ABSTRACT FROM AUTHOR]

• Similar harmonic regression coefficients were derived from modeling and imagery. • Random forest model, MapBiomas and Environmental Rural Register were field masks. • Crop modeling as input for supervised crop classification model had high accuracy. • Crop modeling aggregated with ground data increased model performance. • Spatial variability was treated with crop modeling aggregated with ground data. Crop type mapping is essential for agricultural monitoring, but lack of terrestrial labels (herein termed as ground truth data) limit these models around the globe. In this study, we tested different methods for creating crop type maps for soybean (Glycine max L.) and corn (Zea mays L.), with aims of i) compare ground truth data related to crop modeling; ii) evaluate agricultural field masks generated by Environmental Rural Register, MapBiomas product, and random forest model; iii) evaluate models of a) unsupervised classification, b) supervised classification with ground truth data of 1 year, c) with ground truth data of 2 years, d) supervised classification with crop modeling only, e) with the combination of crop modeling and ground truth data of 1 year, and f) with ground truth data of 2 years; iv) test sample size for training the model utilizing ground truth data and crop modeling in transfer learning approach; and v) examine spatial remote sensing features to guide crop data collection. The APSIM-NG crop model was utilized for generate crop modeling simulations to compare with satellite images harmonic regression of ground truth data. We found similarity of harmonic regression coefficients derived from crop modeling and satellite imagery of Sentinel-2 of the labeled ground truth data for both soybean and corn crops. Agricultural masks showed efficiency for crop area estimation for soybean, with highest accuracy with random forest model. Crop modeling aggregated with growing season data as input for supervised learning presented the greater model performance with overall accuracy of 0.94. Crop area prediction was most accurate for soybean [R2 = 0.93, and mean absolute error (MAE) = 2,052 ha] for the model with the combination of crop modeling and ground truth data of 2 years, and least for corn, [R2 = 0.18, and MAE = 1,146 ha] when only using crop modeling. Model performance was influenced by sample size, with greater accuracy (0.93) for aggregating crop modeling (150 samples) and year data (100 samples). In addition, considering spatial field data variability as model input increased overall accuracy from 0.84 to 0.93, with higher impact when only ground truth data was utilized for input in the model. Our results suggest that these methods offer options for crop type classification when less adequate ground truth data is available and with unsupervised learning. On the other hand, supervised learning utilizing crop modeling with presence of field data did improve model performance overall. [ABSTRACT FROM AUTHOR]

Delayed sowing date of maize (Zea mays L.) has become a broadly adopted practice in the Pampas region of Argentina, because late-sown crops frequently experience a more favorable water balance around flowering and a reduction in the year-to-year yield variation. However, breeding efforts in this region have been focused on early sowing date environments. In addition, there has been no research on genetic progress of grain yield and the underlying attributes under late-sowing conditions. The aim of this study was to compare genetic progress in grain yield and secondary traits for hybrids released between 1980 and 2016 when grown in early- versus late sowing environments in the central Pampas region. Grain yield, its components (kernel number and kernel weight), and its physiological determinants (total shoot biomass and harvest index) were assessed in two contrasting sowing dates during three years. Biomass at silking (B R1) and during the post-silking period (B R1-R6), plant growth rate and source-sink ratios during the critical and the effective kernel-filling periods were also analyzed. Grain yield genetic progress was similar for early (0.9% y-1) relative to late sowing dates (0.8% y-1). Kernel number increase with the year of release (YOR) was more important in early (0.6% y-1) than in late (0.4% y-1) sowings, whereas the opposite trend was documented for kernel weight (0.2% and 0.3% y-1, respectively). Newer hybrids showed greater biomass production in both sowing dates (0.8% y-1 for early, 0.6% y-1 for late) and a slight increase in harvest index in late sowings (0.2% y-1). Breeding effects on shoot biomass production were exclusively attributed to the post-silking period (1.2% y-1 for early and 0.9% y-1 for late), as B R1 remained unchanged with YOR. Assimilate availability per kernel during the effective kernel filling increased with YOR in both sowing dates (1.0% y-1 for early and 0.9% y-1 for late), promoting a longer kernel filling duration. Results demonstrated that breeding efforts focused on early sowings as the main target environment, have also impacted yield gains in late sowings. However, the enhanced importance of kernel weight on grain yield determination among late- than among early sowings will demand future breeding strategies to pay special attention to unfavorable photothermal conditions during kernel filling. • Grain yield genetic progress was similar for early and late sowing dates. • The relative importance of kernel number and kernel weight on yield gains varied with sowing date. • Newer hybrids showed greater post-silking biomass production in both environments. • Post-silking source-sink ratio increased with year of release, promoting a longer kernel filling. [ABSTRACT FROM AUTHOR]

Nitrogen (N) metabolism is a major research target for increasing productivity in crop plants. In maize (Zea mays L.), yield gain over the last few decades has been associated with increased N absorption and utilization efficiency (i.e. grain biomass per unit of N absorbed). However, a dynamical framework is still needed to unravel the role of internal processes such as uptake, allocation, and translocation of N in these adaptations. This study aimed to 1) characterize how genetic enhancement in N efficiency conceals changes in allocation and translocation of N, and 2) quantify internal fluxes behind grain N sources in two historical genotypes under high and low N supply. The genotypes 3394 and P1197, landmark hybrids representing key eras of genetic improvement (1990s and 2010s), were grown under high and low N supply in a two-year field study. Using stable isotope 15N labelling, post-silking nitrogen fluxes were modeled through Bayesian estimation by considering the external N (exogenous-N) and the pre-existing N (endogenous-N) supply across plant organs. Regardless of N availability, P1197 exhibited greater exogenous-N accumulated in leaves and cob-husks. This response was translated to a larger amount of N mobilized to grains (as endogenous-N) during grain-filling in this genotype. Furthermore, the enhanced N supply to leaves in P1197 was associated with increased post-silking carbon accumulation. The overall findings suggest that increased N utilization efficiency over time in maize genotypes was associated with an increased allocation of N to leaves and subsequent translocation to the grains. • Bayesian modelling and 15N analysis were combined to estimate N allocation in maize. • Modern maize plants evidenced an improved post-silking N partitioning to leaves. • Greater N allocation to leaves resulted in an increased post-silking carbon fixation. • Internal translocation to the grains was improved due to larger supply from leaves. [ABSTRACT FROM AUTHOR]

• The uncertainties in the yield response to N can be exploited to improve prediction. • The uncertainties in the yield response to N were largely governed by weather factors. • Soil factors consistently but narrowly (<23%) explained variation of yield response to N. • The economic optimum N rate (EONR) was the most difficult component to predict. • The EONR-uncertainty averaged 50 kg ha−1 increasing at low and high expected values. Development of predictive algorithms accounting for uncertainty in processes underpinning the maize (Zea mays L.) yield response to nitrogen (N) are needed in order to provide new N fertilization guidelines. The aims of this study were to unravel the relative importance of crop management, soil, and weather factors on both the estimate and the size of uncertainty (as a risk magnitude assessment) of the main components of the maize yield response to N: i) yield without N fertilizer (B0); ii) yield at economic optimum N rate (YEONR); iii) EONR; and iv) the N fertilizer efficiency (NFE) at the EONR. Combining Bayesian statistics to fit the N response curves and a machine learning algorithm (extreme gradient boosting) to assess features importance on the predictability of the process, we analyzed data of 730 response curves from 481 site-years (4297 observations) in maize N rate fertilization studies conducted between 1999 and 2020 in the United States and Canada. The EONR was the most difficult attribute to predict, with an average uncertainty of 50 kg N ha−1, increasing towards low (<100 kg N ha−1) and high (>200 kg N ha−1) EONR expected values. Crop management factors such as previous crop and irrigation contributed substantially (∼50%) to the estimation of B0, but minorly to other components of the maize yield response to N. Weather contributed about two-thirds of explained variance of the estimated values of YEONR, EONR, and NFE. Additionally, weather factors governed the uncertainty (72% to 81%) of all components of the N response process. Soil factors provided a consistent but limited (10% to 23%) contribution to explain both expected N response as well as its associated uncertainties. Efforts to improve N decision support tools should consider the uncertainty of models as a type of risk, potential in-season weather scenarios, and develop probabilistic frameworks for improving this data-driven decision-making process of N fertilization in maize crop. [ABSTRACT FROM AUTHOR]

Crop residue decomposition is a process largely mediated by soil microbes. This study aimed to investigate the dynamic of crop residue decomposition, change in fungal community composition and extracellular enzymatic activity in maize (Zea mays L.) residue via utilization of a two-year residue-bags study in north-central China. After the residue was buried in soil, the cumulative residue mass loss rate was 33%, 74%, and 85% after 2, 12, and 24 months, respectively. Total fungal abundance and the activities of beta (β)-glucosidase, β- d -cellobiosidase, and β-xylosidase were greater at the initial stage of decomposition and gradually decreased over time. However, the phenol oxidase activity presented an opposite pattern relative to the hydrolytic enzymes. The fungal community composition was similar in the residue during the initial (0) and 2 months after, with the phylum Ascomycota (classes Sordariomycetes and Dothideomycetes) dominating the community composition for the first two months but then decreased gradually over time. Phyla Basidiomycota , Cercozoa , and classes Eurotiomycetes and Tremellomycetes presented an increasing-decreasing trend during the decomposition process. The phylum Zygomycota (class Leotiomycetes) gradually increased over time and showed greater abundance at the later stage of decomposition; while the fungal community composition was similar in soil and residue samples for the 20–24 months. This study highlights the relevancy of fungal abundance for playing a critical role in residue decomposition, with the composition of this community changing from the dominance of copiotrophic populations in the early stage to oligotrophic populations in the later stage. • Residue decomposed rapidly in the first 2 months and gradually decreased over time. • Fungal abundance and hydrolytic enzymes changed synchronously with residue loss. • Phylum Ascomycota dominated fungal community at the early stage of decomposition. • Basidiomycota and Zygomycota dominated at the intermediate and later stages, respectively. [ABSTRACT FROM AUTHOR]

• Attainable maize yield drives the agronomic optimum of initial N availability (AONav). • Soil texture modifies the curvature of the response models but not the AONav. • For yield environments from 9 to 14 Mg ha-1, the AONav increases by 21 kg N Mg-1. • Fine- and coarse-textured soils show the greatest and the lowest response rates to N. • We could maintain high N use efficiencies by correctly identifying yield environment. The most widely used approach for prescribing fertilizer nitrogen (N) recommendations in maize (Zea Mays L.) in Argentina is based on the relationship between grain yield and the available N (kg N ha−1), calculated as the sum of pre-plant soil NO 3 --N at 0−60 cm depth (PPNT) plus fertilizer N (N f). However, combining covariates related to crop N demand and soil N supply at a large national scale remains unexplored for this model. The aim of this work was to identify yield response patterns associated to yield environment (crop N demand driver) and soil texture (soil N supply driver). A database of 788 experiments (1980−2016) was gathered and analyzed combining quadratic-plateau regression models with bootstrapping to address expected values and variability on response parameters and derived quantities. The database was divided into three groups according to soil texture (fine, medium and coarse) and five groups based on the empirical distribution of maximum observed yields (from Very-Low = <8.5 Mg ha−1 to Very-High = >13.1 Mg ha−1) resulting in fifteen groups. The best model included both, attainable yield environment and soil texture. The yield environment mainly modified the agronomic optimum available N (AON av), with an expected increase rate of ca. 21.4 kg N Mg attainable yield−1, regardless of the soil texture. In Very-Low yield environments, AON av was characterized by a high level of uncertainty, related to a poor fit of the N response model. To a lesser extent, soil texture modified the response curvature but not the AON av , mainly by modifying the response rate to N (Fine > Medium > Coarse), and the N use efficiencies. Considering hypothetical PPNT levels from 40 to 120 kg N ha−1, the expected agronomic efficiency (AEN f) at the AON av varied from 7 to 31, and 9–29 kg yield response kg fertilizer N (N f)−1, for Low and Very-High yield environments, respectively. Similarly, the expected partial factor productivity (PFPN f) at the AON av ranged from 62 to 158, and 55–99 kg yield kg N f −1, for the same yield environments. These results highlight the importance of combining attainable yield environment and soil texture metadata for refining N fertilizer recommendations. Acknowledging the still low N fertilizer use in Argentina, space exists to safely increasing N fertilizer rates, steering the historical soil N mining profile to a more sustainable agro-environmental scenario in the Pampas. [ABSTRACT FROM AUTHOR]

• Uncertainty predicting zero-N maize yield varied between 2.0 to 2.5 Mg ha−1. • Previous crop, irrigation, and soil organic matter were the most relevant predictors. • Spring weather provided key information for refining the yield prediction. Maize (Zea Mays L.) yield responsiveness to nitrogen (N) fertilization depends on the yield under non-limiting N supply as well as on the inherent productivity under zero N fertilizer (Y 0). Understanding the driving factors and developing predictive algorithms for Y 0 will enhance the optimization of N fertilization in maize. Using a random forest algorithm, we analyzed data from 679 maize N fertilization studies (1031 Y 0 observations) conducted between 1999–2019 in the United States and Canada. Predictability of Y 0 was assessed while identifying determinant factors such as soil, crop management, and weather. The inclusion of weather variables as predictors improved the model efficiency (ME) from 51 up to 64 %, and reduced the root mean square error (RMSE) from 2.5 to 2.0 Mg ha−1, 34 to 27 % in relative terms (RRMSE). The most relevant predictors of Y 0 were previous crop, irrigation, and soil organic matter (SOM), while the most influential weather data was linked to the radiation per unit of thermal time (Q quotient) around flowering and spring precipitations. The crop rotation effect resulted in Alfalfa (Medicago sativa L.) as the previous crop with the highest Y 0 level (IQR = 11.5–15.0 Mg ha−1) as compared to annual legumes (IQR = 5.6–10.0 Mg ha−1) and other previous crops (IQR = 3.6–7.8 Mg ha−1). The Q quotient around flowering positively affected Y 0 , while spring precipitations and extreme temperature events during grain filling showed a negative association to Y 0. Overall, these results reinforce the concept that yields are controlled not only by soil N supply but also by factors modifying plant demand and ability to capture N. Lastly, we foresee a promising future for the use of machine learning to address both prediction and interpretation of maize yield to obtain more reliable N guidelines. [ABSTRACT FROM AUTHOR]

Worldwide supply of water and nitrogen (N) are critical to crop yield with multiple interactions that need to be untangled. We used an allometric framework to quantify the dynamic relationships between crop growth, transpiration and N uptake in maize (Zea mays L.) and sorghum (Sorghum bicolor L.) using published data from experiments in France, New Zealand and United States. Our analysis highlighted a two-fold effect of drought on crop growth: a direct effect whereby crop growth is reduced proportionally to the reduction in transpiration, and an indirect effect mediated by a drought-induced crop N deficit, quantified as N nutrition index (NNI), the ratio between actual and critical plant N concentration (the minimum plant N concentration for achieving maximum crop mass). Under drought, sorghum maintained NNI and transpiration efficiency (biomass per unit of transpiration) whereas both NNI and transpiration efficiency declined in drought-stressed maize in comparison to well-watered crops. Nitrogen uptake per unit of transpiration (N/T) is a valuable trait for analyzing the combined effect of water and N deficit on water use efficiency (biomass per unit of evapotranspiration). However, both N/T and crop N concentration decline allometrically with biomass, hence comparisons of genotypes, environments and management practices would be biased unless they are scaled by crop biomass. [ABSTRACT FROM AUTHOR]

• Over the season, shoot N concentration was only affected by N supply. • Over the season, N deficiency decreased shoot S concentration. • Shoot N:S stoichiometry could diagnose N status independently from S supply. • N should not be limiting when using N:S stoichiometry for diagnosing S status. • N and S fertilization increased their nutrient use and recovery efficiencies. Nitrogen (N) and sulfur (S) critical dilution curves and the N:S stoichiometry are useful tools for evaluating nutrient status in maize (Zea mays L.). Nutrient supply for both N and S impact plant nutrient uptake, consequently influencing the overall performance of fertilization diagnostic tools. Interaction between these nutrients has not been studied relative to their nutrient use efficiencies (NUE and SUE), agronomic efficiencies (NAE and SAE), and components of these. The aim of this study was to analyze the effect of N and S fertilization on: i) N and S uptake (N U and S U) dynamics, ii) N and S dilution curves, iii) N:S stoichiometry, and iv) NUE, SUE, NAE, SAE, and their components in maize. Five maize field trials were conducted evaluating the effect of N and S fertilization with two levels for each nutrient (unfertilized and fertilized). Maize shoot biomass and its N and S concentrations were measured from six-leaf to physiological maturity. The main outcomes of this study were: i) averaged across sites, N fertilization increased yield by 41% and S fertilization by 10%; ii) S U was delayed relative to N U ; iii) S fertilization increased shoot S concentration when N was not limiting; iv) N dilution curve was not affected by S supply; v) N:S ratio decreased with increasing shoot biomass and varied with N and S supply; vi) N fertilization improved SUE and SAE via greater S recovery, with a similar S fertilization effect on NUE and NAE. This study demonstrated that both the critical S dilution curve and N:S stoichiometry are useful tools to characterize S status when N is not deficient. Simultaneous N and S management is critical for increasing their use efficiencies, improving crop productivity, and reducing environmental footprint. [ABSTRACT FROM AUTHOR]

• Inconsistent L N fertilization responses were observed across experiments. • Post-silking N uptake (NU Post-Silking) is suggested as an indicator for differential responses to L N. • Nitrogen nutrition index (NNI SILKING) was related with NU Post-Silking changes. • Nitrogen recovery efficiency was enhanced by the utilization of L N fertilization, when properly adopted. Late-season fertilizer N applications in maize (Zea mays L.) is a tactical agronomic practice that seeks to improve the synchrony between plant N demand and soil N supply. While this management practice can increase yield and limit unintended environmental impacts, a quantitative synthesis of the effects of late N applications on grain yield and resource use efficiency is lacking. A meta-analysis was conducted to identify patterns of yield response to late-N fertilization (post tenth-leaf, V10) across site-years studies. The goals of this study were to: 1) quantify the effect of late-season N fertilization on maize yield; and 2) identify an indicator for decision support of late-N fertilization. Published and unpublished sources from 1983 until 2018 were included in the meta-analysis (14 studies; n = 625 treatment means). Early-season (E N , ≈ 100% of fertilizer applied prior to the sixth-leaf, V6) and late-season (L N , < 50% N applied before V10) N fertilization were compared to determine the effect of application timing on yield, N uptake, and N recovery efficiency (REN, N uptake increase per unit of N applied). Across studies, the timing of application of N was not associated with a quantifiable change in maize yield. Due to significant heterogeneity across experiments (I2 = 48%), there is an opportunity to explore plausible effects of the practice when N is limiting (L N improved yield) on yield and effective use of N. Increased post-silking N uptake (>28% of N uptake at maturity occurring after silking) was positively related with yield increases for L N (+577 kg ha−1), and the contrast in REN between L N and E N was 0.04 kg kg−1. Environments prone to a greater N uptake during reproductive stages are proposed to be more suitable for late N applications. Prediction algorithms to inform tactical N management that can both increase productivity and sustainability could be developed provided adequate links between late-season N uptake and growing-season indicators could be established. [ABSTRACT FROM AUTHOR]