Key HPI axis receptors facilitate light adaptive behavior in larval zebrafish.

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.

Late-season corn stalk nitrate measurements across the US Midwest from 2006 to 2018.

The late-season Corn Stalk Nitrate Test (CSNT) is a well-known tool to help evaluate the after-the-fact performance of nitrogen management. The CSNT has the unique ability to distinguish between optimal and excessive corn nitrogen status, which makes it helpful for identifying the over-application of N so that farmers can adjust their future nitrogen decisions. This paper presents a multi-year and multi-location dataset of late-season corn stalk nitrate test measurements across the US Midwest from 2006 to 2018. The dataset consists of 32,025 corn stalk nitrate measurements from 10,675 corn fields. The nitrogen form, total N rate applied, US state, year of harvest, and climatic conditions are included for each corn field. When available, previous crop, manure source, tillage, and timing of N application are also informed. We provide a detailed description of the dataset to make it usable by the scientific community. Data are published through an R package and also available at the USDA National Agricultural Library Ag Data Commons repository and through an interactive website.

Photosynthetic decline in aging perennial grass is not fully explained by leaf nitrogen.

Aging in perennial plants is traditionally observed in terms of changes in end-of-season biomass; however, the driving phenological and physiological changes are poorly understood. We found that 3-year-old (mature) stands of the perennial grass Miscanthus×giganteus had 19-30% lower Anet than 1-year-old M.×giganteus (juvenile) stands; 10-34% lower maximum carboxylation rates of Rubisco and 34% lower light-saturated Anet (Asat). These changes could be related to nitrogen (N) limitations, as mature plants were larger and had 14-34% lower leaf N on an area basis (Na) than juveniles. However, N fertilization restored Na to juvenile levels but compensated only 50% of the observed decline in leaf photosynthesis with age. Comparison of leaf photosynthesis per unit of leaf N (PNUE) showed that mature stands had at least 26% lower PNUE than juvenile stands across all N fertilization rates, suggesting that other factors, besides N, may be limiting photosynthesis in mature stands. We hypothesize that sink limitations in mature stands could be causing feedback inhibition of photosynthesis which is associated with the age-related decline in photosynthesis.

Development of a data-assimilation system to forecast agricultural systems: A case study of constraining soil water and soil nitrogen dynamics in the APSIM model.

As we face today's large-scale agricultural issues, the need for robust methods of agricultural forecasting has never been clearer. Yet, the accuracy and precision of our forecasts remains limited by current tools and methods. To overcome the limitations of process-based models and observed data, we iteratively designed and tested a generalizable and robust data-assimilation system that systematically constrains state variables in the APSIM model to improve forecast accuracy and precision. Our final novel system utilizes the Ensemble Kalman Filter to constrain model states and update model parameters at observed time steps and incorporates an algorithm that improves system performance through the joint estimation of system error matrices. We tested this system at the Energy Farm, a well-monitored research site in central Illinois, where we assimilated observed in situ soil moisture at daily time steps for two years and evaluated how assimilation impacted model forecasts of soil moisture, yield, leaf area index, tile flow, and nitrate leaching by comparing estimates with in situ observations. The system improved the accuracy and precision of soil moisture estimates for the assimilation layers by an average of 42% and 48%, respectively, when compared to the free model. Such improvements led to changes in the model's soil water and nitrogen processes and, on average, increased accuracy in forecasts of annual tile flow by 43% and annual nitrate loads by 10%. Forecasts of aboveground measures did not dramatically change with assimilation, a fact which highlights the limited potential of soil moisture as a constraint for a site with no water stress. Extending the scope of previous work, our results demonstrate the power of data assimilation to constrain important model estimates beyond the assimilated state variable, such as nitrate leaching. Replication of this study is necessary to further define the limitations and opportunities of the developed system.

On-Farm Trials Reveal Significant but Uncertain Control of Botrytis cinerea by Aureobasidium pullulans and Potassium Bicarbonate in Organic Grapevines.

Botrytis cinerea, a fungal pathogen that causes gray mold on grapes, can decrease yield, substantially reduce wine quality, and therefore cause significant economic losses. In a context of increasing awareness of environmental and human health, biopesticides are a potential alternative to synthetic chemical treatments to produce grapes and wine in compliance with high food standards. However, the effectiveness of biopesticides is not well known and more research is needed to help winegrowers assess their ability to control wine diseases. Our study aims to assess the efficacy of two commercial biopesticides, based on potassium bicarbonate and Aureobasidium pullulans, in reducing the incidence of gray mold (i.e., the proportion of grape bunches that are diseased). We use data from an on-farm trial network managed over 3 years (from 2014 to 2016) in a major wine producing region located in Southwestern France, and fit Bayesian generalized linear multilevel models able to take the variability of treatment effect across trials into account. The fitted models were then used to estimate the efficacy on incidence as a function of the severity (i.e., the proportion of diseased grape berries in a bunch) in an untreated plot in order to determine if the effectiveness of the treatments depends on the disease pressure. At average disease severity (i.e., 3%), the efficacy on disease incidence at the network level was equal to 20% [95% CI = (-0.1; 37.3)] and 13% [95% CI = (0.2; 24.7)] for potassium bicarbonate and A. pullulans, respectively. For both biopesticides, the efficacy on incidence for a new site-year is highly uncertain, but potassium bicarbonate had a lower uncertainty and a lower application cost compared to A. pullulans. Our results confirm that potassium bicarbonate is an interesting biopesticide under farming conditions in organic vineyards in southwestern France, but the amount of uncertainty points to the need for further research.

Lengthening of maize maturity time is not a widespread climate change adaptation strategy in the US Midwest.

Increasing temperatures in the US Midwest are projected to reduce maize yields because warmer temperatures hasten reproductive development and, as a result, shorten the grain fill period. However, there is widespread expectation that farmers will mitigate projected yield losses by planting longer season hybrids that lengthen the grain fill period. Here, we ask: (a) how current hybrid maturity length relates to thermal availability of the local climate, and (b) if farmers are shifting to longer season hybrids in response to a warming climate. To address these questions, we used county-level Pioneer brand hybrid sales (Corteva Agriscience) across 17 years and 650 counties in 10 Midwest states (IA, IL, IN, MI, MN, MO, ND, OH, SD, and WI). Northern counties were shown to select hybrid maturities with growing degree day (GDD°C) requirements more closely related to the environmentally available GDD compared to central and southern counties. This measure, termed "thermal overlap," ranged from complete 106% in northern counties to a mere 63% in southern counties. The relationship between thermal overlap and latitude was fit using split-line regression and a breakpoint of 42.8°N was identified. Over the 17-years, hybrid maturities shortened across the majority of the Midwest with only a minority of counties lengthening in select northern and southern areas. The annual change in maturity ranged from -5.4 to 4.1 GDD year-1 with a median of -0.9 GDD year-1 . The shortening of hybrid maturity contrasts with widespread expectations of hybrid maturity aligning with magnitude of warming. Factors other than thermal availability appear to more strongly impact farmer decision-making such as the benefit of shorter maturity hybrids on grain drying costs, direct delivery to ethanol biorefineries, field operability, labor constraints, and crop genetics availability. Prediction of hybrid choice under future climate scenarios must include climatic factors, physiological-genetic attributes, socio-economic, and operational constraints.

A nonlinear mixed-effects modeling approach for ecological data: Using temporal dynamics of vegetation moisture as an example.

Increasingly, often ecologist collects data with nonlinear trends, heterogeneous variances, temporal correlation, and hierarchical structure. Nonlinear mixed-effects models offer a flexible approach to such data, but the estimation and interpretation of these models present challenges, partly associated with the lack of worked examples in the ecological literature.We illustrate the nonlinear mixed-effects modeling approach using temporal dynamics of vegetation moisture with field data from northwestern Patagonia. This is a Mediterranean-type climate region where modeling temporal changes in live fuel moisture content are conceptually relevant (ecological theory) and have practical implications (fire management). We used this approach to answer whether moisture dynamics varies among functional groups and aridity conditions, and compared it with other simpler statistical models. The modeling process is set out "step-by-step": We start translating the ideas about the system dynamics to a statistical model, which is made increasingly complex in order to include different sources of variability and correlation structures. We provide guidelines and R scripts (including a new self-starting function) that make data analyses reproducible. We also explain how to extract the parameter estimates from the R output.Our modeling approach suggests moisture dynamic to vary between grasses and shrubs, and between grasses facing different aridity conditions. Compared to more classical models, the nonlinear mixed-effects model showed greater goodness of fit and met statistical assumptions. While the mixed-effects approach accounts for spatial nesting, temporal dependence, and variance heterogeneity; the nonlinear function allowed to model the seasonal pattern.Parameters of the nonlinear mixed-effects model reflected relevant ecological processes. From an applied perspective, the model could forecast the time when fuel moisture becomes critical to fire occurrence. Due to the lack of worked examples for nonlinear mixed-effects models in the literature, our modeling approach could be useful to diverse ecologists dealing with complex data.

Policies for Ecological Intensification of Crop Production.

Ecological intensification aims to increase crop productivity by enhancing biodiversity and associated ecosystem services, while minimizing the use of synthetic inputs and cropland expansion. Policies to promote ecological intensification have emerged in different countries, but they are still scarce and vary widely across regions. Here, we propose ten policy targets that governments can follow for ecological intensification.

Maximum soil organic carbon storage in Midwest U.S. cropping systems when crops are optimally nitrogen-fertilized.

Nitrogen fertilization is critical to optimize short-term crop yield, but its long-term effect on soil organic C (SOC) is uncertain. Here, we clarify the impact of N fertilization on SOC in typical maize-based (Zea mays L.) Midwest U.S. cropping systems by accounting for site-to-site variability in maize yield response to N fertilization. Within continuous maize and maize-soybean [Glycine max (L.) Merr.] systems at four Iowa locations, we evaluated changes in surface SOC over 14 to 16 years across a range of N fertilizer rates empirically determined to be insufficient, optimum, or excessive for maximum maize yield. Soil organic C balances were negative where no N was applied but neutral (maize-soybean) or positive (continuous maize) at the agronomic optimum N rate (AONR). For continuous maize, the rate of SOC storage increased with increasing N rate, reaching a maximum at the AONR and decreasing above the AONR. Greater SOC storage in the optimally fertilized continuous maize system than in the optimally fertilized maize-soybean system was attributed to greater crop residue production and greater SOC storage efficiency in the continuous maize system. Mean annual crop residue production at the AONR was 22% greater in the continuous maize system than in the maize-soybean system and the rate of SOC storage per unit residue C input was 58% greater in the monocrop system. Our results demonstrate that agronomic optimum N fertilization is critical to maintain or increase SOC of Midwest U.S. cropland.

Autumnal leaf senescence in Miscanthus × giganteus and leaf [N] differ by stand age.

Poor first winter survival in Miscanthus × giganteus has been anecdotally attributed to incomplete first autumn senescence, but these assessments never paired first-year with older M. × giganteus in side-by-side trials to separate the effect of weather from stand age. Here CO2 assimilation rate (A), photosystem II efficiency (ΦPSII), and leaf N concentration ([N]) were used to directly compare senescence in first, second, and third-year stands of M. × giganteus. Three M. × giganteus fields were planted with eight plots, one field each in 2009, 2010, and 2011. To quantify autumnal leaf senescence of plants within each stand age, photosynthetic and leaf [N] measurements were made twice weekly from early September until a killing frost. Following chilling events (daily temperature averages below 10 °C), photosynthetic rates in first year plants rebounded to a greater degree than those in second- and third-year plants. By the end of the growing season, first-year M. × giganteus had A and ΦPSII rates up to 4 times greater than third-year M. × giganteus, while leaf [N] was up to 2.4 times greater. The increased photosynthetic capability and leaf N status in first-year M. × giganteus suggests that the photosynthetic apparatus was not dismantled before a killing frost, thus potentially limiting nutrient translocation, and may explain why young M. × giganteus stands do not survive winter when older stands do. Because previous senescence research has primarily focused on annual or woody species, our results suggest that M. × giganteus may be an interesting herbaceous perennial system to investigate the interactive effects of plant ageing and nutrient status on senescence and may highlight management strategies that could potentially increase winter survival rates in first-year stands.

Evaluation of synergistic antioxidant potential of complex mixtures using oxygen radical absorbance capacity (ORAC) and electron paramagnetic resonance (EPR).

Previous research has demonstrated that certain combinations of compounds result in a decrease in toxic or pro-oxidative effects, previously noted when compounds were administered singly. Thus, there is a need to study many complex interactions further. Two in vitro techniques [electron paramagnetic resonance (EPR) and oxygen radical absorbance capacity (ORAC) assays] were used in this study to assess pro- and antioxidant capacity and synergistic potential of various compounds. Rutin, p-coumaric acid, abscisic acid, ascorbic acid, and a sugar solution were evaluated individually at various concentrations and in all 26 possible combinations at concentrations found in certain foods (honey or papaya), both before and after simulated digestion. EPR results indicated sugar-containing combinations provided significantly higher antioxidant capacity; those combinations containing sugars and ascorbic acid demonstrated synergistic potential. The ORAC assay suggested additive effects, with some combinations having synergistic potential, although fewer combinations were significantly synergistic after digestion. Finally, ascorbic acid, caffeic acid, quercetin, and urate were evaluated at serum-achievable levels. EPR analysis did not demonstrate additive or synergistic potential, although ORAC analysis did, principally in combinations containing ascorbic acid.

Wasp gene expression supports an evolutionary link between maternal behavior and eusociality.

The presence of workers that forgo reproduction and care for their siblings is a defining feature of eusociality and a major challenge for evolutionary theory. It has been proposed that worker behavior evolved from maternal care behavior. We explored this idea by studying gene expression in the primitively eusocial wasp Polistes metricus. Because little genomic information existed for this species, we used 454 sequencing to generate 391,157 brain complementary DNA reads, resulting in robust hits to 3017 genes from the honey bee genome, from which we identified and assayed orthologs of 32 honey bee behaviorally related genes. Wasp brain gene expression in workers was more similar to that in foundresses, which show maternal care, than to that in queens and gynes, which do not. Insulin-related genes were among the differentially regulated genes, suggesting that the evolution of eusociality involved major nutritional and reproductive pathways.