Auxin-based Herbicide Program for Weed Control in Auxin Resistant Soybean

Soybean [Glycine max (L.) Merr.] cultivars resistant to synthetic auxin herbicides have provided another mode of action for the postemergence broadleaf weed control. This field study was conducted at three South Dakota locations [Northeast, NERF; east-central, ARF; and Southeast, SERF) in 2019 and two locations (ARF and SERF) in 2020. The Enlist E3 and Roundup Ready 2 Xtend cultivars were planted at three dates (early, mid-, and late season) to examine weed control, agronomic characteristics, nodulation, and yield. Preemergence (PRE) treatment was flumioxazin + metribuzin + S-metolachlor + glyphosate + pendimethalin. Two postemergence (POST) treatments, based on cultivar, were compared with PRE-only. The PREonly treatment had numerous grasses {including green foxtail [Setaria viridis (L.) P. Beauv.] and yellow foxtail [S. pumila (Poir.) Roem. & Schult.], volunteer corn (Zea mays L.), barnyard grass [Echinochola crus-galli (L.) Beauv.], large crabgrass [Digitaria sanguinalis (L.) Scop.], woolly cupgrass [Eriochloa villosa (Thunb.) Kunth]} and broadleaf weeds (including redroot pigweed [Amaranthus retroflexus L.], common lambsquarters [Chenopodium album L.], waterhemp [Amaranthus rudis Sauer]) with high density and biomass. POST treatments controlled most of the broadleaf species, although some grasses remained. Yields were similar within a location and year, although differences occurred among planting dates. In 2019, planting date did not influence final yield at ARF (average yield 3,084 kg ha−1). Yield was greatest for the early (NERF) and mid-planting dates (NERF and SERF) compared with late-season planting. In 2020, dry conditions occurred, and yields at ARF and SERF were lowest for the late-season plantings (ranging from 37 to 73% lower depending on cultivar) compared with the early season planting. In 2020, dicamba + glyphosate treatment of the Xtend cultivar had 10% (ARF) and 20% (SERF) greater yield than the acifluorfen + clethodim treatment

Microarray and Growth Analyses Identify Differences and Similarities of Early Corn Response to Weeds, Shade, and Nitrogen Stress

Weed interference with crop growth is often attributed to water, nutrient, or light competition; however, specific physiological responses to these stresses are not well described. This study\u27s objective was to compare growth, yield, and gene expression responses of corn to nitrogen (N), low light (40% shade), and weed stresses. Corn vegetative parameters from V2 to V12 stages, yield parameters, and gene expression using transcriptome (2008) and quantitative polymerase chain reaction (qPCR) (2008/09) analyses at V8 were compared among the stresses and with nonstressed corn. N stress did not affect vegetative parameters, although grain yield was reduced by 40% compared with nonstressed plants. Shade, present until V2, reduced biomass and leaf area \u3e 50% at V2, and recovering plants remained smaller than nonstressed plants at V12. However, grain yields of shade-stressed and nonstressed plants were similar, unless shade remained until V8. Weed stress reduced corn growth and yield in 2008 when weeds remained until V6. In 2009, weed stress until V2 reduced corn vegetative growth, but yield reductions occurred only if weed stress remained until V6 or later. Principle component analysis of differentially expressed genes indicated that shade and weed stress had more similar gene expression patterns to each other than they did to nonstressed or N-stressed tissues. However, corn grown in N-stressed conditions shared 252 differentially expressed genes with weed-stressed plants. Ontologies associated with light/photosynthesis, energy conversion, and signaling were down-regulated in response to all three stresses. Shade and weed stress clustered most tightly together, based on gene expression, but shared only three ontologies, O-METHYLTRANSFERASE activity (lignification processes), POLY(U)-BINDING activity (posttranscriptional gene regulation), and stomatal movement. Based on morphologic and genomic observations, weed stress to corn was not explained by individual effects of N or light stress. Therefore, we hypothesize that these stresses share limited signaling mechanisms

Corn Response to Competition: Growth Alteration vs. Yield Limiting Factors

Competition mechanisms among adjacent plants are not well understood. This study compared corn growth and yield responses to water, N, and shade at 74,500 plants ha−1 (1×) with responses to water and N when planted at 149,000 plant ha−1 Plant biomass, leaf area, chlorophyll content, reflectance, and enzyme expression (transcriptome analysis) were measured at V-12. Grain and stover yields were measured with grain analyzed for 13C isotopic discrimination (Δ) and N concentration. At V-12, 60% shade plants had increased chlorophyll and reduced leaf area and height compared to full sun plants. In the 2× treatment, plants had 11% less chlorophyll than 1× plants with leaf area and height similar to 60% shade plants. At harvest, plants in the 2× treatment were smaller, had increased water and N use efficiency, and an 11% per hectare yield increase compared with the 1× unstressed treatment. Per-plant yields from 60% shade and 2× treatments were 50% less than 1× unstressed treatment. Yield reduction in shaded plants was attributed to light stress. Lower yield in the 2× treatment was attributed to a population-density induced 20% decrease in the red/near-infrared (NIR) ratio, which resulted in downregulation of C4 carbon metabolism enzymes (phosphoenolpyruvate carboxykinase, phosphoenolpyruvate carboxylase, and pyruvate orthophosphate dikinase). Although the net impact of high plant density and shade stress on per-plant yield were similar, the stress compensation mechanisms differed

Quantification and Machine Learning Based N2O-N and CO2-C Emissions Predictions from a Decomposing Rye Cover Crop

Cover crops improve soil health and reduce the risk of soil erosion. However, their impact on the carbon dioxide equivalence (CO2e) is unknown. Therefore, objective of this two-year study was to quantify the effect of cover crop-induced differences in soil moisture, temperature, organic C, and microorganisms on CO2e and to develop machine learning algorithms that predict daily N2O-N and CO2-C emissions. The prediction models tested were multiple linear regression (MLR), partial least square regression (PLSR), support vector machine (SVM), random forest (RF), and artificial neural network (ANN). Models’ performance was accessed using R2 , RMSE and MAE. Rye (secale cereale) was dormant seeded in mid-October and in the following spring it was terminated at corn’s (Zea mays) V4 growth stage. Soil temperature, moisture, and N2O-N and CO2-C emissions were measured near continuously from soil thaw to harvest in 2019 and 2020. Prior to termination, the cover crop decreased N2O-N emissions by 34% (p=0.05) and over the entire season, N2O-N emissions from cover crop and no cover crop treatments were similar (p=0.71). Based on N2O-N and CO2-C emissions over the entire season and the estimated fixed cover crop carbon remaining in the soil, the partial CO2e were -1,061 and 496 kg CO2e ha-1 in the cover crop and no cover crop treatments, respectively. The RF algorithm explained more of the daily N2O-N (73%) and CO2-C (85%) emissions variability during validation than the other models. Across models, the most important variables were temperature and the amount of cover crop-C added to the soil

Common Sunflower Seedling Emergence across the U.S. Midwest

Predictions of weed emergence can be used by practitioners to schedule POST weed management operations. Common sunflower seed from Kansas was used at six Midwestern U.S. sites to examine the variability that 16 climates had on common sunflower emergence. Nonlinear mixed effects models, using a flexible sigmoidal Weibull function that included thermal time, hydrothermal time, and a modified hydrothermal time (with accumulation starting from January 1 of each year), were developed to describe the emergence data. An iterative method was used to select an optimal base temperature (Tb) and base and ceiling soil matric potentials (ψb and ψc) that resulted in a best-fit regional model. The most parsimonious model, based on Akaike\u27s information criterion (AIC), resulted when Tb = 4.4 C, and ψb = −20000 kPa. Deviations among model fits for individual site years indicated a negative relationship (r = −0.75; P \u3c 0.001) between the duration of seedling emergence and growing degree days (Tb = 10 C) from October (fall planting) to March. Thus, seeds exposed to warmer conditions from fall burial to spring emergence had longer emergence periods

Winter Cereal Rye Cover Crop Decreased Nitrous Oxide Emissions During Early Spring

Despite differences between the cover crop growth and decomposition phases, few greenhouse gas (GHG) studies have separated these phases from each other. This study’s hypothesis was that a living cover crop reduces soil inorganic N concentrations and soil water, thereby reducing N2O emissions. We quantified the effects of a fall-planted living cereal rye (Secale cereale L.) cover crop (2017, 2018, 2019) on the following spring’s soil temperature, soil water, water-filled porosity (WFP), inorganic N, and GHG (N2O-N and CO2–C) emissions and compared these measurements to bare soil. The experimental design was a randomized complete block, where years were treated as blocks. Rye was fall planted in 2017, 2018, and 2019, but mostly emerged the following spring. The GHG emissions were near-continuously measured from early spring through June. Rye biomass was 1,049, 428, and 2,647 kg ha–1 in 2018, 2019, and 2020, respectively. Compared to the bare soil, rye reduced WFP in the surface 5 cm by 29, 15, and 26% in 2018, 2019, and 2020 and reduced soil NO3–N in surface 30 cm by 53% in 2019 (p = .04) and 65% in 2020 (p = .07), respectively. Rye changed the N2O and CO2 frequency emission signatures. It also reduced N2O emissions by 66% but did not influence CO2–C emissions during the period prior to corn (Zea mays L.) emergence (VE). After VE, rye and bare soils N2O emissions were similar. These results suggest that nitrous oxide (N2O-N) sampling protocols must account for early season impacts of the living cover

Local Conditions, Not Regional Gradients, Drive Demographic Variation of Giant Ragweed (Ambrosia trifida) and Common Sunflower (Helianthus annuus) Across Northern U.S. Maize Belt

Knowledge of environmental factors influencing demography of weed species will improve understanding of current and future weed invasions. The objective of this study was to quantify regional-scale variation in vital rates of giant ragweed and common sunflower. To accomplish this objective, a common field experiment was conducted across seven sites between 2006 and 2008 throughout the north central U.S. maize belt. Demographic parameters of both weed species were measured in intra- and interspecific competitive environments, and environmental data were collected within site-years. Site was the strongest predictor of belowground vital rates (summer and winter seed survival and seedling recruitment), indicating sensitivity to local abiotic conditions. However, biotic factors influenced aboveground vital rates (seedling survival and fecundity). Partial least squares regression (PLSR) indicated that demography of both species was most strongly influenced by thermal time and precipitation. The first PLSR components, both characterized by thermal time, explained 63.2% and 77.0% of variation in the demography of giant ragweed and common sunflower, respectively; the second PLSR components, both characterized by precipitation, explained 18.3% and 8.5% of variation, respectively. The influence of temperature and precipitation is important in understanding the population dynamics and potential distribution of these species in response to climate change

Landscape Features Impact on Soil Available Water, Corn Biomass, and Gene Expression during the Late Vegetative Stage

Crop yields at summit positions of rolling landscapes often are lower than backslope yields. The differences in plant response may be the result of many different factors. We examined corn (Zea mays L.) plant productivity, gene expression, soil water, and nutrient availability in two landscape positions located in historically high (backslope) and moderate (summit and shoulder) yielding zones to gain insight into plant response differences. Growth characteristics, gene expression, and soil parameters (water and N and P content) were determined at the V12 growth stage of corn. At tassel, plant biomass, N content, 13C isotope discrimination (Δ), and soil water was measured. Soil water was 35% lower in the summit and shoulder compared with the lower backslope plots. Plants at the summit had 16% less leaf area, biomass, and N and P uptake at V12 and 30% less biomass at tassel compared with plants from the lower backslope. Transcriptome analysis at V12 indicated that summit and shoulder-grown plants had 496 downregulated and 341 upregulated genes compared with backslope-grown plants. Gene set and subnetwork enrichment analyses indicated alterations in growth and circadian response and lowered nutrient uptake, wound recovery, pest resistance, and photosynthetic capacity in summit and shoulder-grown plants. Reducing plant populations, to lessen demands on available soil water, and applying pesticides, to limit biotic stress, may ameliorate negative water stress responses

Planting date, cultivar, seed treatment, and seeding rate effects on soybean growth and yield

Soybean [Glycine max (L.) Merr.] yield is a function of many factors including genetic attributes of the cultivar, environmental conditions, and management practices. Temporally variable weather patterns in North America, especially in the northern Great Plains, have resulted in the re-examination of how spring production practices interact with the environmental conditions to influence yield. This study evaluated the impact of four plantings dates, four seeding rates, and two soybean maturity groups (MGs) using treated and untreated (control) seed on soybean growth, seed yield, and composition. The study was conducted at Volga, SD, in 2014, 2015, and 2016. The planting dates in the study ranged from early May to early July and the four seeding rates were 247,000; 333,500; 420,000; 506,500 seeds ha−1. Stand establishment decreased as seeding rate increased irrespective of planting date. The number of growing degree days (GDDs) to R1 decreased with delayed planting. Delayed planting also decreased the number of GDDs to R8, the length of the reproductive phase (R1−R8), and seed yield. Delayed planting decreased seed yield for both MGs but the rate of decrease was greater forMG 2.4 than MG 1.4. Seed treatment increased seed yield irrespective of planting date. Seed protein was variable among planting dates and between MGs while seed oil decreased with delayed planting. The research documents the impact of delayed planting on soybean yield and quality and highlights the importance of early planting in soybean irrespective of maturity group and growth habit

Calculating Soil Organic Turnover at Different Landscape Position in Precision Conservation

An important aspect of precision conservation is assessing changes in soil health and soil organic carbon (SOC) across landscapes. Carbon (C) cycling can be determined using carbon flux towers, modeling, and by experimentally measuring budgets. Once the carbon budgets are understood, this information can be used to assess the value of implementing precision conservation and the potential impacts of targeted residue harvesting on soil health. This chapter provides a review of methods to determine carbon budgets, and the potential impacts of crop residue harvesting on SOC maintenance across landscapes. The chapter also provides examples on how to convert point carbon budget measurements into a precision conservation assessment