A Bayesian approach for estimating the uncertainty on the contribution of nitrogen fixation and calculation of nutrient balances in grain legumes.

BACKGROUND: The proportion of nitrogen (N) derived from the atmosphere (Ndfa) is a fundamental component of the plant N demand in legume species. To estimate the N benefit of grain legumes for the subsequent crop in the rotation, a simplified N balance is frequently used. This balance is calculated as the difference between fixed N and removed N by grains. The Ndfa needed to achieve a neutral N balance (hereafter θ ) is usually estimated through a simple linear regression model between Ndfa and N balance. This quantity is routinely estimated without accounting for the uncertainty in the estimate, which is needed to perform formal statistical inference about θ . In this article, we utilized a global database to describe the development of a novel Bayesian framework to quantify the uncertainty of θ . This study aimed to (i) develop a Bayesian framework to quantify the uncertainty of θ , and (ii) contrast the use of this Bayesian framework with the widely used delta and bootstrapping methods under different data availability scenarios. RESULTS: The delta method, bootstrapping, and Bayesian inference provided nearly equivalent numerical values when the range of values for Ndfa was thoroughly explored during data collection (e.g., 6-91%), and the number of observations was relatively high (e.g., ≥ 100 ). When the Ndfa tested was narrow and/or sample size was small, the delta method and bootstrapping provided confidence intervals containing biologically non-meaningful values (i.e. < 0% or > 100%). However, under a narrow Ndfa range and small sample size, the developed Bayesian inference framework obtained biologically meaningful values in the uncertainty estimation. CONCLUSION: In this study, we showed that the developed Bayesian framework was preferable under limited data conditions ─by using informative priors─ and when uncertainty estimation had to be constrained (regularized) to obtain meaningful inference. The presented Bayesian framework lays the foundation not only to conduct formal comparisons or hypothesis testing involving θ , but also to learn about its expected value, variance, and higher moments such as skewness and kurtosis under different agroecological and crop management conditions. This framework can also be transferred to estimate balances for other nutrients and/or field crops to gain knowledge on global crop nutrient balances.

Soybean seed yield, protein, and oil concentration for a modern and old genotype under varying row spacings.

Progress in soybean (Glycine max L.) breeding has led to a reduction in optimal seeding rates due to enhanced branching capacity over time. However, less is known about the changes in canopy architecture between old and modern soybean genotypes at varying row spacing and their impact on yield and seed quality through the main stem and branches. Therefore, this study aimed to i) evaluate yield and seed quality responses of an old and modern soybean genotype at different row spacings and ii) examine the yield and seed quality of branches and the main stem. Trials were conducted in Kansas (United States, US) during 2020 and 2021, comparing two genotypes (old, released in 1980, and modern, released in 2013) at four row spacings (0.19, 0.38, 0.76, and 1.52 m) under rainfed conditions. Seed yield and quality (protein and oil concentrations, %) were assessed at the end of each growing season. In 2021, both genotypes had low and similar yields at all row spacings (averaging 2481 kg ha-1) with 2.5 % less protein on branches compared to the main stem. However, 2022 resulted in a high-yielding environment, with the modern yielding 50 % more (3584 kg ha-1) than the old (2315 kg ha-1) genotype in narrow row spacings (<0.38 m). Additionally, the modern genotype showed a three-fold greater contribution to yield from branches (1113 kg ha-1) relative to the old genotype (379 kg ha-1). Despite the high yields observed in narrow rows, the modern genotype maintained protein levels. These results highlight the importance of row spacing as a key management practice for improving yield while maintaining protein levels in high yield conditions.

Detecting sorghum aphid infestation in grain sorghum using leaf spectral response.

Sorghum aphid, Melanaphis sorghi (Theobald) have become a major economic pest in sorghum causing 70% yield loss without timely insecticide applications. The overarching goal is to develop a monitoring system for sorghum aphids using remote sensing technologies to detect changes in plant-aphid density interactions, thereby reducing scouting time. We studied the effect of aphid density on sorghum spectral responses near the feeding site and on distal leaves from infestation and quantified potential systemic effects to determine if aphid feeding can be detected. A leaf spectrometer at 400-1000 nm range was used to measure reflectance changes by varying levels of sorghum aphid density on lower leaves and those distant to the caged infestation. Our study results demonstrate that sorghum aphid infestation can be determined by changes in reflected light, especially between the green-red range (550-650 nm), and sorghum plants respond systemically. This study serves as an essential first step in developing more effective pest monitoring systems for sorghum aphids, as leaf reflection sensors can be used to identify aphid feeding regardless of infestation location on the plant. Future research should address whether such reflectance signatures can be detected autonomously using small unmanned aircraft systems or sUAS equipped with comparable sensor technologies.

Benefits and trade-offs of replacing inorganic fertilizer by organic substrate in crop production: A global meta-analysis.

Replacing inorganic fertilizer with organic substrate contributes to sustainable agricultural production capacity. However, the effects of organic substitution regimes (OSR) on global crop productivity, soil carbon (C) and nitrogen (N) losses and biofertility as function of environmental variables have not been systematically quantified. Here, we have conducted a meta-analysis of these effects using field data (211 papers with 852 observations) collected around the world. Results indicated that OSR increased crop productivity (3.04 %) and soil biofertility (soil qMBC, qMBN, microbial richness, Shannon and functionality by 11.4 %, 21.1 %, 10.2 %, 3.95 %, and 38.5 %, respectively), and reduced soil N losses (N2O emissions, NH3 volatilization and soil N leaching by 26.5 %, 26.1 %, and 33.8 %, respectively), but increased CO2 emissions (19.4 %), and paddy fields CH4 emissions (41.2 %). N rate was an important factor influencing crop productivity and soil biofertility response to OSR, and crop productivity and soil biofertility had a greater positive response at moderate substitution rates in acid soil and long-term trials, but full substitution significantly decreased crop yield. Furthermore, the increase in soil biofertility and crop yield saturated in ~10-14 and ~ 22 years after organic substrate input. The emissions of CO2, CH4, and N2O significantly increased with increasing substitution rates, while the opposite was true for N leaching. The NH3 volatilization response to OSR presented a positive effect in acidic and coarse texture soil. OSR was more beneficial in mitigating soil C and N loss response (except CO2 emissions) in uplands compared to paddy fields. Therefore, implementation of OSR requires site-specific strategies to better achieve a balance between increasing crop production and reducing environmental benefits. Given that the OSR improvement varies depending on environmental variables, we propose a predictive model to initially assess the potential for OSR improvement. This study will provide scientific guidance on the reasonable application of organic substrate in agroecosystems.

A meta-analysis of hairy vetch as a previous cover crop for maize.

Background: The use of hairy vetch (Vicia villosa Roth.) as cover crop is increasing worldwide. Hairy vetch can contribute as a nitrogen (N) source with potential to impact subsequent high N demanding cereals such as maize (Zea mays L.). Contrasting literature results emphasize the need for a global synthesis analysis to quantify changes in maize yield after hairy vetch. Objectives: A meta-analysis was conducted to i) quantify maize yield response to hairy vetch as previous crop, ii) explore hairy vetch influence on fertilized and non-N fertilized maize yields, and iii) assess the tillage and environment factors on maize yield response to hairy vetch. Methods: The global systematic search yielded 23 publications selected by the following criteria, i) hairy vetch dry matter at the end of the season, ii) maize grain yield, and iii) experimental design with (Mzhv) and without (Mzcontrol) hairy vetch treatments. Information such as N fertilization for maize, N accumulation in hairy vetch, organic matter, and tillage before maize sowing were recorded. Hairy vetch effects (effect size) were expressed as a ratio (percentage of grain yield variation in Mzhv/Mzcontrol). Results: Under non-N fertilization (n = 9), results revealed hairy vetch had mostly a positive effect, ranging from 13 to 45% (n = 6). In contrast, N-fertilized maize (n = 20) showed a high chance of neutral effects (n = 12), moderate probability of positive yield impact (7 to 38%, n = 6), and a low likelihood of negative effects (-32 and -17%, n = 2). Notably, maize yields improved by 21-25% when the N accumulation in hairy vetch ranged from 95 to 150 kg ha-1 and N rate from 0 to 120 kg ha-1. Non-N-fertilized maize exhibited a 14% increase in response in no-till systems and a 31% increase with conventional tillage. Conclusion: This study summarizes potential benefits of hairy vetch preceding maize. Yet, the heterogeneous outcomes deserve further exploration in terms of environment and management factors.

Predicting corn tiller development in restrictive environments can be achieved to enhance defensive management decision tools for producers.

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

Early-season plant-to-plant spatial uniformity can affect soybean yields.

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

zmm28 transgenic maize increases both N uptake- and N utilization-efficiencies.

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.

Climate Change and Management Impacts on Soybean N Fixation, Soil N Mineralization, N2O Emissions, and Seed Yield.

Limited knowledge about how nitrogen (N) dynamics are affected by climate change, weather variability, and crop management is a major barrier to improving the productivity and environmental performance of soybean-based cropping systems. To fill this knowledge gap, we created a systems understanding of agroecosystem N dynamics and quantified the impact of controllable (management) and uncontrollable (weather, climate) factors on N fluxes and soybean yields. We performed a simulation experiment across 10 soybean production environments in the United States using the Agricultural Production Systems sIMulator (APSIM) model and future climate projections from five global circulation models. Climate change (2020-2080) increased N mineralization (24%) and N2O emissions (19%) but decreased N fixation (32%), seed N (20%), and yields (19%). Soil and crop management practices altered N fluxes at a similar magnitude as climate change but in many different directions, revealing opportunities to improve soybean systems' performance. Among many practices explored, we identified two solutions with great potential: improved residue management (short-term) and water management (long-term). Inter-annual weather variability and management practices affected soybean yield less than N fluxes, which creates opportunities to manage N fluxes without compromising yields, especially in regions with adequate to excess soil moisture. This work provides actionable results (tradeoffs, synergies, directions) to inform decision-making for adapting crop management in a changing climate to improve soybean production systems.

Environment Characterization in Sorghum (Sorghum bicolor L.) by Modeling Water-Deficit and Heat Patterns in the Great Plains Region, United States.

Environmental characterization for defining the target population of environments (TPE) is critical to improve the efficiency of breeding programs in crops, such as sorghum (Sorghum bicolor L.). The aim of this study was to characterize the spatial and temporal variation for a TPE for sorghum within the United States. APSIM-sorghum, included in the Agricultural Production Systems sIMulator software platform, was used to quantify water-deficit and heat patterns for 15 sites in the sorghum belt. Historical weather data (∼35 years) was used to identify water (WSP) and heat (HSP) stress patterns to develop water-heat clusters. Four WSPs were identified with large differences in the timing of onset, intensity, and duration of the stress. In the western region of Kansas, Oklahoma, and Texas, the most frequent WSP (∼35%) was stress during grain filling with late recovery. For northeast Kansas, WSP frequencies were more evenly distributed, suggesting large temporal variation. Three HSPs were defined, with the low HSP being most frequent (∼68%). Field data from Kansas State University sorghum hybrid yield performance trials (2006-2013 period, 6 hybrids, 10 sites, 46 site × year combinations) were classified into the previously defined WSP and HSP clusters. As the intensity of the environmental stress increased, there was a clear reduction on grain yield. Both simulated and observed yield data showed similar yield trends when the level of heat or water stressed increased. Field yield data clearly separated contrasting clusters for both water and heat patterns (with vs. without stress). Thus, the patterns were regrouped into four categories, which account for the observed genotype by environment interaction (GxE) and can be applied in a breeding program. A better definition of TPE to improve predictability of GxE could accelerate genetic gains and help bridge the gap between breeders, agronomists, and farmers.

Kernel weight contribution to yield genetic gain of maize: a global review and US case studies.

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.

From use efficiency to effective use of nitrogen: A dilemma for maize breeding improvement.

Maize (Zea mays L.) breeding is continuously moving forward yield gains for many fields crops, increasing dependency to technology such as high input seed costs and high use of nitrogen (N) fertilizers. For this crop, breeding improvement led to concomitantly enhancing N recovery and uptake but following a similar ratio relative to the plant biomass (W) and nitrogen nutrition index (NNI, as actual to critical N concentration) levels. The aim of this review is to provides new insights related to the true gains of N use efficiency (NUE) for maize over time and to propose new direction to target improvement on the effective use of N. Thus, the increase in fertilizer N for modern hybrids to attain greater yields lead to a greater dependency to N fertilization and potentially increasing the overall environmental risks for N losses associated to this practice. Contrarily to the improvement based on NUE, improving the intrinsic N uptake capacity (more N uptake per unit of biomass) is needed to maximize yield and the effective use of N. These results highlight that crop breeding should refocus to directly target an increase on the effective use of N, increasing the efficiency on using environmental resources while seeking for improving attainable yields. SYNOPSIS: Enhancing resilience of our production systems is critical for food security goals. This review highlights the need of investment on directly targeting improvement of the effective use of N not only to improve efficiency but to reduce the dependency to fertilization and environmental risks.

Post-silking 15N labelling reveals an enhanced nitrogen allocation to leaves in modern maize (Zea mays) genotypes.

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.

Breeding effects on canopy light attenuation in maize: a retrospective and prospective analysis.

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.

Corn yield components can be stabilized via tillering in sub-optimal plant densities.

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.

Revisiting Biological Nitrogen Fixation Dynamics in Soybeans.

Biological nitrogen (N) fixation is the most relevant process in soybeans (Glycine max L.) to satisfy plant N demand and sustain seed protein formation. Past studies describing N fixation for field-grown soybeans mainly focused on a single point time measurement (mainly toward the end of the season) and on the partial N budget (fixed-N minus seed N removal), overlooking the seasonal pattern of this process. Therefore, this study synthesized field datasets involving multiple temporal measurements during the crop growing season to characterize N fixation dynamics using both fixed-N (kg ha-1) and N derived from the atmosphere [Ndfa (%)] to define: (i) time to the maximum rate of N fixation (ß2), (ii) time to the maximum Ndfa (α2), and (iii) the cumulative fixed-N. The main outcomes of this study are that (1) the maximum rate of N fixation was around the beginning of pod formation (R3 stage), (2) time to the maximum Ndfa (%) was after full pod formation (R4), and (3) cumulative fixation was positively associated with the seasonal vapor-pressure deficit (VPD) and growth cycle length but negatively associated with soil clay content, and (4) time to the maximum N fixation rate (ß2) was positively impacted by season length and negatively impacted by high temperatures during vegetative growth (but positively for VPD, during the same period). Overall, variation in the timing of the maximum rate of N fixation occurred within a much narrower range of growth stages (R3) than the timing of the maximum Ndfa (%), which varied broadly from flowering (R1) to seed filing (R5-R6) depending on the evaluated studies. From a phenotyping standpoint, N fixation determinations after the R4 growth stage would most likely permit capturing both maximum fixed-N rate and maximum Ndfa (%). Further investigations that more closely screen the interplay between N fixation with soil-plant-environment factors should be pursued.

Crop modeling defines opportunities and challenges for drought escape, water capture, and yield increase using chilling-tolerant sorghum.

Many crop species, particularly those of tropical origin, are chilling sensitive, so improved chilling tolerance can enhance production of these crops in temperate regions. For the cereal crop sorghum (Sorghum bicolor L.), early planting and chilling tolerance have been investigated for >50 years, but the potential value or tradeoffs of this genotype × management change have not been formally evaluated with modeling. To assess the potential of early planted chilling-tolerant grain sorghum in the central US sorghum belt, we conducted CERES-Sorghum simulations and characterized scenarios under which this change would be expected to enhance (or diminish) drought escape, water capture, and yield. We conducted crop growth modeling for full- and short-season hybrids under rainfed systems that were simulated to be planted in very early (April), early (May 15), and normal (June 15) planting dates over 1986-2015 in four locations in Kansas representative of the central US sorghum belt. Simulations indicated that very early planting will generally lead to lower initial soil moisture, longer growing periods, and higher evapotranspiration. Very early planting is expected to extend the growing period by 20% for short- or full-season hybrids, reduce evaporation during fallow periods, and increase plant transpiration in the two-thirds of years with the highest precipitation (mean > 428 mm), leading to 11% and 7% increase grain yield for short- and full-season hybrids, respectively. Thus, in this major sorghum growing region, very early and early planting could reduce risks of terminal droughts, extend seasons, and increase rotation options, suggesting that further development of chilling-tolerant hybrids is warranted.

An integrated approach of field, weather, and satellite data for monitoring maize phenology.

Efficient, more accurate reporting of maize (Zea mays L.) phenology, crop condition, and progress is crucial for agronomists and policy makers. Integration of satellite imagery with machine learning models has shown great potential to improve crop classification and facilitate in-season phenological reports. However, crop phenology classification precision must be substantially improved to transform data into actionable management decisions for farmers and agronomists. An integrated approach utilizing ground truth field data for maize crop phenology (2013-2018 seasons), satellite imagery (Landsat 8), and weather data was explored with the following objectives: (i) model training and validation-identify the best combination of spectral bands, vegetation indices (VIs), weather parameters, geolocation, and ground truth data, resulting in a model with the highest accuracy across years at each season segment (step one) and (ii) model testing-post-selection model performance evaluation for each phenology class with unseen data (hold-out cross-validation) (step two). The best model performance for classifying maize phenology was documented when VIs (NDVI, EVI, GCVI, NDWI, GVMI) and vapor pressure deficit (VPD) were used as input variables. This study supports the integration of field ground truth, satellite imagery, and weather data to classify maize crop phenology, thereby facilitating foundational decision making and agricultural interventions for the different members of the agricultural chain.

Benchmarking Nutraceutical Soybean Composition Relative to Protein and Oil.

The aim of this study was to explore relationships between protein, oil, and seed weight with seed nutraceutical composition, focused on total isoflavone (TI) and total tocopherol (TT) contents across genotypic and environmental combinations in soybean. We conducted a synthesis-analysis of peer-reviewed published field studies reporting TI, TT, protein, oil, and seed weight (n = 1,908). The main outcomes from this synthesis-analysis were: (i) relationship of TI-to-protein concentration was positive, though for the upper boundary, TI decreases with increases in protein; (ii) relationship of TT-to-oil concentration was positive, but inconsistent when oil was expressed in mg per seed; and (iii) as seed weight increased, TI accumulation was less than proportional relative to protein concentration and TT decreased more proportional relative to oil concentration. Association between nutraceuticals and protein, oil, and seed weight for soybean reported in the present study can be used as a foundational knowledge for soybean breeding programs interested on predicting and selecting enhanced meal isoflavone and/or oil tocopherol contents.