Introduction Process-based crop models such as the Agricultural Production Systems sIMulator Next Generation (APSIM-NG) can simulate crop growth, phenology, and yield under diverse environmental and management conditions, supporting climate-smart agriculture strategies aimed at improving productivity and resilience. However, accurate calibration and validation are required to ensure reliable predictions across cultivars and nitrogen (N) management scenarios.Methods We evaluated APSIM-NG performance for simulating winter wheat cultivar responses to N rate using field experiments conducted in Nebraska during the 2020/21 and 2021/22 growing seasons. Trials followed a randomized complete block design with two cultivars (LCS and WB), four N rates (0, 56, 112, and 168 kg N ha(-)(1)), and three replications. Observations included phenology, grain yield, protein content, shoot biomass, carbon-to-nitrogen ratio, soil nitrate and ammonium, soil moisture, and weather variables. Model calibration targeted cultivar-specific phenology, biomass, yield, and protein content. Validation was conducted using grain yield data from 29 site-year combinations across five Nebraska counties spanning six growing seasons (2017-2022). Model accuracy was evaluated using RMSE, RRMSE, and mean bias error.Results Calibration improved model performance, with well to moderate accuracy for phenology (RRMSE = 2.1-2.2%; RMSE = 3-5 days), grain yield (15-24%), protein content (8-11%), and grain N uptake (11-13%). APSIM-NG moderately captured cultivar differences in leaf N uptake, with RRMSE values of 27% for LCS and 33% for WB. Validation results showed good performance for grain yield in both cultivars (RRMSE = 14% for LCS and 19% for WB). Yield response to N was simulated well for LCS (RRMSE = 18% at the economic optimum N rate) and moderately for WB (32%).Discussion Overall, APSIM-NG demonstrated well to moderate performance in simulating phenology, yield, and grain N dynamics across winter wheat cultivars. These results highlight the model's utility for evaluating N management strategies and supporting climate-smart decision-making aimed at improving nitrogen use efficiency and adaptation to climate variability in wheat systems.

Increasing soil organic carbon (SOC) in croplands is crucial for enhancing soil health and improving nutrient cycling. Anaerobic digestate solids (ADS) are increasingly applied as an organic amendment, yet the mechanisms controlling how ADS-derived carbon (C) contributes to persistent SOC pools remain uncertain. It is unclear how soil C loading—the ratio of mineral-associated organic carbon (MAOC) to silt + clay—regulates SOC formation following ADS additions. Here, we distinguish between two complementary metrics: marginal formation, the increase in an SOC pool per unit of ADS-C applied, and formation efficiency, the proportion of C input incorporated into that pool. We hypothesized that increasing C loading reduces marginal MAOC formation by decreasing reactive mineral surfaces but has no effect on particulate organic carbon (POC). We conducted a 6-month incubation using two agricultural soils of similar texture but contrasting C loading levels (low, Soil L; high, Soil H) to quantify ADS and native SOC decomposition, enzyme responses, and MAOC and POC formation across four ADS-C input rates. Consistent with prior field studies, increasing C loading decreased the SOC formation efficiency of ADS. However, the underlying mechanisms differed from our expectations: marginal MAOC formation increased (from 0.13 to 0.21), whereas marginal POC formation decreased (from 0.18 to 0.10), with higher C loading. These contrasting responses were more consistent with microbial controls associated with resource availability than with predictions based on C saturation alone. Soil H—characterized by higher concentrations of labile C and nutrients—likely supported greater microbial growth and higher carbon use efficiency, conditions favorable for microbial biomass production and residue accumulation. In contrast, Soil L had lower C and nutrient availability, which constrained microbial metabolism and favored ADS-derived C accumulation in the POC pool. Overall, our study highlights that MAOC and POC formation are jointly shaped by resource availability and microbial physiology, rather than mineral saturation alone. These findings underscore the importance of integrating multiple control factors when evaluating SOC dynamics under ADS management.

Genetic studies based on end-of-season measurements focus only on the outcome of a complex and dynamic process. Uncovering the genetic basis underlying the temporal dynamics of plant height will enhance our understanding of the genotype-to-phenotype relationship. Here, we conducted functional mapping to investigate the temporal dynamics of plant height using the time-series data extracted from unmanned aerial vehicle (UAV)-based RGB imagery from two sorghum populations. Significant correlations were found between the UAV-derived measurements and manual measurements. We modeled the growth trajectory using a logistic function. Among quantitative trait loci (QTLs) identified by mapping with the growth curve parameters as derived traits, several were co-localized with known genes controlling plant height. To further visualize the temporal patterns of genetic effects, we used the logistic function to estimate the height of each genotype at 5 d intervals. Genome scans of the model-estimated heights detected QTLs with dynamic effect changes across development. Persistent QTLs, co-localizing with Dw1, Dw2, Dw3, and qHT7.1, were detectable starting from 40 d after planting, whereas several transient QTLs were only detectable within specific shorter periods or during some growing seasons. These findings enabled us to generate a conceptual figure to depict six potential dynamic patterns of persistent and transient QTLs underlying growth trajectories.

Bumble bees' (Bombus spp.) thoracic muscles can generate substantial heat through non-flight thermogenesis (NFT) to mediate flight under cool conditions and incubate brood. Bumble bee castes vary tremendously in life history, physiology and body size, making them an excellent system to investigate sources of intraspecific variation in NFT. This study measured trends in NFT-mediated rewarming across castes (queens, workers and males) of a bumble bee species (Bombus impatiens), while investigating the effects of body mass and seasonality. We used a thermal imaging camera to quantify changes in thoracic temperature as bees recovered from chill coma and used generalized additive models to compare rewarming curves across groups. All bees showed two distinct phases of rewarming, which we suggest represent passive and active warming, driven by biophysical heat exchange and physiological thermogenesis, respectively. While all castes reached similar equilibrium temperatures, each showed significantly different rewarming patterns. Mass effects were most pronounced for males, with small males (<0.15 g) failing to achieve above-ambient body temperatures. In addition, fall queens reached higher thoracic temperatures than spring queens, suggesting NFT may also vary seasonally. The adaptive significance of this variation requires further exploration, but our data demonstrate heretofore unknown individual variation and size-related constraints in thermal physiology in bumble bees.

Processing yield monitor data remains a challenging task in on-farm research, particularly when evaluating yield response to inputs, such as seed and nitrogen (N). This study evaluated the comparative performance of two yield monitor processing algorithms. The first is simple and is based upon empirical thresholds, and the second is RITAS (Rectangle creation, Intersection assignment, Tessellation, Apportioning, and Smoothing) and is a constructive and computationally expensive algorithm. The effects of the different processing algorithms on the model estimates were compared in a simulation study, and two case studies with experimental data were used to demonstrate the applicability of these algorithms in an on-farm setting. In the simulation study, the simple algorithm produced less precise and accurate estimates when compared to RITAS. For instance, the standard deviations of the agronomic optimum nitrogen rate and economic optimum nitrogen rate (EONR) were 83% and 51% greater when using simple, compared to RITAS. Furthermore, when estimating the EONR, the simple algorithm presented a bias of -8 kg N ha(-1). The experimental data results from a strip trial and a checkerboard trial showed differences of 24 kg N ha(-1) and 26 x 10(3) seeds ha(-1) in the estimate of optimum agronomic nitrogen and seeding rates, respectively, between the two processing algorithms. This study indicates that the choice of algorithm for processing yield monitor data can greatly influence the estimates retrieved from on-farm experimental data. The RITAS algorithm produced more accurate and precise estimates than simple, indicating its potential for broad use in on-farm research.

Anaerobic digestion can produce renewable natural gas and is a viable alternative to conventional sources. When anaerobic digesters are coupled with agricultural systems, the resulting anaerobic digestate solids (ADS) after biogas production can be applied to fields as a fertilizer and an organic soil amendment. Therefore, ADS can potentially increase soil organic carbon (SOC) stock and improve soil fertility. To better understand the impacts of ADS on SOC accumulation and nutrient release, we conducted a 120‐day laboratory incubation using four ADS rates (0, 2.5, 5, and 10 Mg C ha −1 ) in typical loamy and sandy soils of Iowa. We measured respired CO 2 ‐C, δ 13 CO 2 ‐C, dissolved reactive phosphorus (DRP), and extractable nitrogen (N). ADS‐derived CO 2 increased, but SOC‐derived CO 2 decreased as the ADS rate increased, indicating a negative priming effect (average of −78%). The C balance in the soil, defined as C inputs minus C respiration, significantly increased with ADS rates. Using reasonable bulk density and mixing depth assumptions, applying the medium ADS rate to soil would accumulate more SOC in the sandy than in the loamy soil (3 vs. 2.2 Mg C ha −1 ). Extractable N and DRP release rates were affected by ADS rates but in opposite directions. DRP increased while extractable N decreased with ADS additions. We conclude that ADS is a bioavailable source of C and nutrients for soil microbes that decreases short‐term inorganic N, increases phosphorus availability, and leads to SOC accrual.

The drive to increase seed yield in soybean [Glycine max (L.) Merr.] has traditionally overshadowed the exploration of biomass partitioning and the compositional characteristics of plant residue traits such as leaves, petioles, stems, and pods. The exploration of biomass partitioning and the compositional characteristics of plant residue traits in soybean provide insights into plant nutrient allocation strategies that can be utilized to increase crop productivity and improve management practices for maximizing yields and sustainability. Recognizing this gap, our study aimed to investigate the variability in these traits across 32 genetically diverse soybean genotypes cultivated over 2 years in central Iowa. Through detailed collection and analysis of vegetative parts at critical growth stages (R1, R4, and R8), we assessed both biomass traits and their chemical compositional characteristics, focusing on soybean residue traits to enhance soil health and their importance in soybean cropping systems. We present broad sense heritability estimates for accumulated (R8) organ biomass (0.61-0.87) and residue carbon nitrogen composition (0.74) in soybeans. The large variation and high heritability suggest breeding strategies to optimize variety development via biomass and residue traits. Utilizing the Agriculture Production Systems sIMulator, we conducted a sensitivity analysis to evaluate the impact of soybean residue quality on soil nutrient cycling and its effects on the subsequent maize [Zea mays L.] crop. The study underscores the importance of soybean residue management, emphasizing the need for integrated approaches in breeding and agricultural practices that utilize the genetic diversity of these traits.

Providing accurate and precise nitrogen (N) fertilizer recommendations remains a significant challenge. To arrive at a recommendation, researchers traditionally conduct hundreds or thousands of field experiments measuring the grain yield response to varying N fertilizer rates. A statistical model is then fit to the data to calculate the agronomic optimum nitrogen rate (AONR)-the fertilizer N rate that maximizes crop yield. We evaluated the impact of excluding individual fertilizer rates on the AONR estimate and its precision using a mixed-effects quadratic-plateau model on a previously published dataset of 49 maize (Zea mays L.) fertility trials with eight N fertilizer rates. When excluding the control treatment (0 kg N ha-1), the AONR deviated from the AONR calculated using all eight rates from 0 to 59 kg N ha-1 for individual sites-a larger change than when any other N rate was excluded. Excluding the control treatment also caused the greatest loss in the AONR estimate precision, with an average standard error increase of +43% without the control compared to +23% for other N rates. Furthermore, our simulations confirmed these findings and showed the largest losses of accuracy and precision when the control treatment was excluded. These trends in bias and precision persisted with different simulated experimental designs. Our results demonstrate the importance of including the control treatment in N fertility trials designed to estimate the AONR. Therefore, we recommend including a control treatment for a more precise N fertilizer recommendation program, especially in on-farm research.

Various works have quantitatively characterized the effects of environmental and management factors on Miscanthus x$\times$ giganteus Greef et Deu (mxg) yield and, therefore, anticipated land requirement per unit production. However, little work has addressed the effects of cutting height, which may significantly contribute to the difference between the standing aboveground biomass at harvest (i.e., biological yield) and harvested yield. This study quantitatively characterized the effect of cutting height using a replicated nitrogen trial of a 5-year-old mxg stand in southeast Iowa and related this information to observations of cutting height in nearby commercial fields. Nitrogen fertilizer did not significantly change the relationship of the stem segment mass to length, and overall, a 1-cm stem segment contributes 0.5% of the total stem biomass within the bottom 44 cm of the stem. This results in an average harvest loss of 15% of the aboveground standing biomass when cutting at 30 cm, typically seen in commercial mxg fields in eastern Iowa. Cutting height should be considered when accurately predicting commercial mxg harvest yields and changes in soil organic carbon in a commercial mxg agroecosystem.

Crop growth rate is a critical physiological trait for forage and bioenergy crops like sorghum [Sorghum bicolor (L.) Moench], influencing overall crop productivity, particularly in photoperiod-sensitive (PS) types. Crop growth rate studies focus on either a physiological approach utilizing a few genotypes to analyze biomass accumulation or a genetic approach characterizing easily scorable proxy traits in larger populations. Thus, the genetic control of crop growth rate in terms of biomass accumulation is poorly understood in PS sorghum. In this study, we monitored biomass accumulation in a diverse panel comprising 269 PS sorghum accessions in two growing seasons. We performed sequential samplings at 11 timepoints, separating leaves from stems. For the total biomass and each fraction, we applied the beta growth function to determine the maximum crop growth rate (cm), maximum biomass accumulation (wmax), and time to cm (tm). Significant genetic variability was observed for all three parameters. Our analysis identified a practical window for cm assessment through accumulated biomass at 60-70 days after planting. Genome-wide association analysis suggested distinct and independent genetic controls of leaf and stem biomass accumulation, both physically and temporally. Common genomic regions were discovered controlling wmax and cm of stem and total biomass. These results provide new insights into the genetic control of crop growth rate, highlighting promising genomic regions for functional validation. This research also offers practical applications for plant breeding programs demonstrating the feasibility of selecting superior genotypes for both early and late biomass accumulation to enhance crop productivity.

Soybean seed composition has long been a subject of study, not only due to its importance to the oil market, but also because variations in seed protein and oil content impact meal quality. Previous analyses of historical data (1948–1998) examined trends in seed protein and oil concentrations over time across different maturity groups (MGs) in the United States. Our study extends the previous analysis through the more recent period of 1999–2022. We found that seed protein concentration significantly declined over time in short (MG0, MG1, and MG2) and mid (MG3, MG4, and MG5) MGs at a rate of 0.04% and 0.06% per year, respectively. In contrast, protein levels in long MGs (MG6, MG7, and MG8) increased at a rate of 0.10% per year. For seed oil concentration, however, no distinct differences among MG classes were observed; instead, oil concentration showed a consistent increase of 0.11% per year across all classes. Additionally, we identified an inverse relationship between protein and oil concentrations, with protein decreasing by 0.26% for every 1% increase in oil, regardless of MG category. These insights helped to identify predictors for modeling seed protein and oil concentrations, allowing the assessment for additional information to increase prediction accuracy. We found that a simpler model including latitude, longitude, sowing date, and mean temperature from sowing to flowering performed similarly to more complex models that included additional environmental variables. Moreover, nonlinear relationships between predictors appear to play a significant role, as models capable of capturing these interactions outperformed linear approaches.

High-throughput crop phenotyping (HTP) in soybean (Glycine max) has been used to estimate seed yield with varying degrees of accuracy. Research in this area typically makes use of different machine-learning approaches to predict seed yield based on crop images with a strong focus on analytics. On the other hand, a significant part of the soybean breeding community still utilizes linear approaches to relate canopy traits and seed yield relying on parsimony. Our research attempted to address the limitations related to interpretability, scope and system comprehension inherent in previous modelling approaches. We utilized a combination of empirical and simulated data to augment the experimental footprint as well as to explore the combined effects of genetics (G), environments (E) and management (M). We use flexible functions without assuming a pre-determined response between canopy traits and seed yield. Factors such as soybean maturity date, duration of vegetative and reproductive periods, harvest index, potential leaf size, planting date and plant population affected the shape of the canopy-seed yield relationship as well as the canopy optimum values at which selection of high yielding genotypes should be conducted. This work demonstrates that there are avenues for improved application of HTP in soybean breeding programs if similar modelling approaches are considered.

The field of precision agriculture relies on collecting and processing several types of data to assess temporal and spatial variability. We developed the pacu R package to provide a comprehensive and transparent framework for precision agriculture applications. The package includes functions to process and visualize yield monitor data from production and experimental fields. In addition, pacu facilitates the retrieval, processing, and visualization of satellite images from Copernicus Data Space. Lastly, there are functions to retrieve, summarize, and visualize long-term weather data. The current package is intended to facilitate the access by researchers and agronomists to routine precision agriculture computational utilities.

The vertebrate stress response (SR) is mediated by the hypothalamic-pituitary-adrenal (HPA) axis and contributes to generating context appropriate physiological and behavioral changes. Although the HPA axis plays vital roles both in stressful and basal conditions, research has focused on the response under stress. To understand broader roles of the HPA axis in a changing environment, we characterized an adaptive behavior of larval zebrafish during ambient illumination changes. Genetic abrogation of glucocorticoid receptor (nr3c1) decreased basal locomotor activity in light and darkness. Some key HPI axis receptors (mc2r [ACTH receptor], nr3c1), but not nr3c2 (mineralocorticoid receptor), were required to adapt to light more efficiently but became dispensable when longer illumination was provided. Such light adaptation was more efficient in dimmer light. Our findings show that the HPI axis contributes to the SR, facilitating the phasic response and maintaining an adapted basal state, and that certain adaptations occur without HPI axis activity.

Context or problem: Nitrogen (N) fertilizer is among the costliest inputs to maize (Zea mays L.) production, and the most challenging input to predict the optimum application for enhanced productivity while preventing loss to the environment. Objective: This study aimed to determine if late spring maize stalk sap nitrate-N concentrations measured during vegetative growth stages can be used to guide in-season N fertilizer input decisions. Methods: Maize stalk sap nitrate-N concentrations were measured at the seven to nine-leaf (V7-V9) developmental stage across eight sites (location-crop rotation-year) in Iowa. Each site received four to eight pre-plant N fertilizer rates. Results: At each site, the stalk nitrate-N concentration consistently increased with the N fertilization rate. Relative grain yield was positively related to sap nitrate concentration. In addition, there was a positive relationship between sap nitrate concentration, tissue total N concentration, late spring soil nitrate test, and end-of-season maize stalk nitrate test. Overall, across all sites, the sap nitrate-N concentration that indicated N sufficiency (i.e., N supply sufficient to achieve the highest relative yield) spanned a relatively narrow range (715-893 mg N L-1 sap) compared to the full observed range (22-1478 mg N L-1 sap). Conclusion: Observations from this multi-site-year study suggest that stalk sap nitrate concentration has the potential to aid in-season N fertilizer application recommendations. Thus, it deserves further study considering other environmental and management factors that potentially affect the sap nitrate N concentrations. Implications or significance: A stalk sap nitrate test along with rapid, reliable, and low-cost nitrate sensors can create unprecedented databases to optimize soil fertility and plant nutrition.

The agricultural sector is responsible for substantial amounts of greenhouse gas emissions that exacerbate climate change. Such greenhouse gas emissions from upland crops are difficult to abate because they are dominated by nitrous oxide (N2O) production from soil processes. Strategies to reduce these emissions focus on N fertilizer management, and there is a widespread assumption that legume crops, which do not receive N fertilizer, emit little N2O. Here we show that this assumption is incorrect; approximately 40% of N2O emissions from the most extensive cropping system in North America-the maize-soybean rotation-occur during the soybean phase. Yet, due to the lack of N fertilizer input, opportunities for emissions abatement from the soybean phase are unclear. Using models of cropping systems, we developed a strategy that combines cover-crop management and earlier planting of extended growth soybean varieties to reduce emissions from soybean production by 33%. These practices, which complement N fertilizer management in maize, are widely accessible and represent an immediate, climate-smart strategy to reduce nitrous oxide emissions from soybean production, thus not only contributing to climate-change mitigation but also maintaining productivity while adapting to changing weather patterns. Soil processes involved in agricultural practices emit considerable levels of nitrous oxide, which detrimentally contribute to climate change. This study explores strategies to reduce nitrous oxide emissions while maintaining crop productivity in the US maize-soybean rotational cropping system.

Understanding historical changes in root depth attributes is needed for crop productivity and sustainability assessments, but such information is rare. We explored whether newer maize (Zea mays L.) hybrids grow roots faster and deeper than older hybrids and quantified the role of management and environment on root trait expression. We measured root front velocity (RFV) and maximum root depth in 11 Bayer Crop Science legacy hybrids released from 1983 to 2017 across five environments in the US Corn Belt during 2021 and 2022. Root depth was measured weekly during vegetative stages with manual probes and the maximum root depth at crop harvest with a Giddings probe. Results indicated that the RFV and maximum root depth slightly increased with the year of hybrid release (0.13% per year, p = 0.1) at 8.7 plants m(-2). Historical increases in plant density from 4.7 to 8.7 plants m(-2) lowered RFV and maximum root depth, but the new hybrids compensated for this loss, resulting in 4% higher RFV and 3% higher maximum depth when comparing systems from 1983 to 2017. The environment strongly influenced root trait expression (>41%). Rain anomaly and soil bulk density explained a portion of this variation. We found a linear relationship between root depth and leaf number (R-2 = 0.95) and a nonlinear relationship between RFV and maximum root depth (R-2 = 0.77), which can stimulate crop model improvements. Faster and deeper roots were not correlated with maize yields in our environments. This study enhances our understanding of maize breeding impacts on root traits.

The vertebrate stress response (SR) is mediated by the hypothalamic-pituitary-adrenal (HPA) axis and contributes to generating context appropriate physiological and behavioral changes. Although the HPA axis plays vital roles both in stressful and basal conditions, research has focused on the response under stress. To understand broader roles of the HPA axis in a changing environment, we characterized an adaptive behavior of larval zebrafish during ambient illumination changes. The glucocorticoid receptor (nr3c1) was necessary to maintain basal locomotor activity in light and darkness. The HPA axis was required to adapt to light more efficiently but became dispensable when longer illumination was provided. Light adaptation was more efficient in dimmer light and did not require the mineralocorticoid receptor (nr3c2). Our findings show that the HPA axis contributes to the SR at various stages, facilitating the phasic response and maintaining an adapted basal state, and that certain adaptations occur without HPA axis activity.

Farmers and researchers continue to question the impact of rye (Secale cereale L.) cover crops (RCC) on the optimal N fertilizer rate and grain yield of corn (Zea mays L.). In addition, minimal research has addressed the role of N fertilizer timing on reducing corn N stress and yield loss following a RCC. In this research, our objectives were to evaluate corn N fertilizer requirement following a RCC and different N fertilizer timings. Trials were established at three locations (2017-20) to evaluate corn response to N fertilizer rate (0 - 303 kg N ha(-1)) following a RCC and no RCC with preplant and split-applied N fertilizer. A lower plant-available N supply, indicated by lower soil inorganic N and corn chlorophyll content, as well as a reduced plant stand were observed following a RCC. A RCC reduced corn grain yield by 20% at the 0 kg N ha(-1) due to apparent N deficiency, but did not reduce corn yield at the agronomic optimum N rate (AONR) or the economic optimum N rate (EONR). The AONR and EONR were statistically similar regardless of RCC presence and N application timing. However, corn chlorophyll content, agronomic efficiency, and yield was increased following a split N application. In addition, a quadratic-plateau regression analysis indicated greater yield increase per unit N applied for a split application than a preplant application. Our results suggest that farmers in similar production environments may benefit from a split N application to improve corn N use efficiency and yield regardless of the presence of a RCC. However, split application may be appealing following a RCC, where lower plant-available N supply requires more efficient fertilizer delivery to limit a higher N fertilizer rate.