Early Reproductive Growth Research Articles

Impact of flooding at the early reproductive growth stage on soybean yield and seed composition

AbstractFlooding stress is a growing threat to global soybean [Glycine max (L.) Merr.] production as the frequency and intensity of extreme precipitations are increasing due to climate change. Soybean is highly sensitive to flooding and substantial yield losses are observed due to a cascade of negative physiological responses induced by hypoxia. Hence, there is a pressing need for the development of flood‐tolerant genotypes. This study evaluated the grain yield and seed protein and oil content of 31 soybean genotypes over 2 years under both non‐flooding and flooding conditions, where flooding entailed a 4‐day partial water submergence during the early reproductive growth stages R1/R2. Mixed‐effects linear models were utilized to assess the impact of flood damage scores (FDSs, 1–4 scale) on observed phenotypes, as well as differences in observed phenotypes between tolerant, moderate, and susceptible genotypes across flooding and non‐flooding treatments. No significant impact of FDS was observed for seed protein and oil content. In addition, no significant differences in these phenotypes were observed between flooding and non‐flooding treatments across the various genotype categories. On average, for each unit increase in FDS, grain yield decreased by 432.7 kg ha−1 (17.4%). Tolerant genotypes experienced roughly 33% yield losses between flooding and non‐flooding treatments, while moderate and susceptible genotypes experienced 44% and 51% yield losses, respectively. The advancements in genomics and phenomics are promising for the identification and incorporation of novel flood‐tolerant alleles through plant breeding, potentially mitigating flooding‐induced yield losses across diverse environmental conditions.

Effects of Climate Change and Drought Tolerance on Maize Growth.

Climate change is affecting all regions of the world with different climates, and the scale of damage is increasing due to the occurrence of various natural disasters. In particular, maize production is highly affected by abnormal climate events such as heat waves and droughts. Increasing temperatures can accelerate growth and shorten the growing season, potentially reducing productivity. Additionally, enhanced temperatures during the ripening period can accelerate the process, reducing crop yields. In addition, drought stress due to water deficit can greatly affect seedling formation, early plant growth, photosynthesis, reproductive growth, and yield, so proper water management is critical to maize growth. Maize, in particular, is tall and broad-leaved, so extreme drought stress at planting can cause leaves to curl and stunt growth. It is important to understand that severe drought can have a detrimental effect on the growth and reproduction of maize. In addition, high temperatures caused by drought stress can inhibit the induction of flowering in male flowers and cause factors that interfere with pollen development. It is therefore important to increase the productivity of all food crops, including maize, while maintaining them in the face of persistent drought caused by climate change. This requires a strategy to develop genetically modified crops and drought-tolerant maize that can effectively respond to climate change. The aim of this paper is to investigate the effects of climate change and drought tolerance on maize growth. We also reviewed molecular breeding techniques to develop drought-tolerant maize varieties in response to climate change.

Evaluating the Effects of Flooding Stress during Multiple Growth Stages in Soybean

Flooding is becoming an increasing concern for soybean (Glycine max [L.] Merr.) production worldwide due to the sensitivity of most cultivars grown today to flood stress. Flooding can stunt plant growth and limit yield, causing significant economic loss. One sustainable approach to improve performance under flood stress is to develop flood-tolerant soybean cultivars. This study was conducted to evaluate soybean genotypes for the response to flood stress at three critical growth stages of production—germination, early vegetative growth (V1 and V4), and early reproductive growth (R1). The results demonstrated that stress imposed by flooding significantly affected soybean yield for each growth stage studied. The average germination rate over the various treatments ranged from 95% to 46%. Despite the poor germination rates after the extended flood treatments, the flood-tolerant genotypes maintained a germination rate of >80% after 8 h of flooding. The germination rate of the susceptible genotypes was significantly lower, ranging 58–63%. Imposing flood stress at the V1 and V4 growth stage also resulted in significant differences between the tolerant and susceptible genotypes. Genotypes with the highest level of flood tolerance continually outperformed the susceptible genotypes with an average 30% decrease in foliar damage based on visual scoring and a 10% increase in biomass. The yield of the tolerant genotypes was also on average 25% higher compared to the susceptible genotypes. These results suggest that breeding for flood tolerance in soybean can increase resiliency during crucial growth stages and increase yield under flood conditions. In addition, the genotypes developed from this research can be used as breeding stock to further make improvements to flood tolerance in soybean.

Recovery of Grain Yield and Protein with Fertilizer Application Post Nitrogen Stress in Winter Wheat (Triticum aestivum L.)

Unfavorable weather conditions and lack of appropriate farm machinery often delay N application. This results in nitrogen (N) deficiency during the vegetative and early reproductive growth stages of winter wheat. The objective of this study was to evaluate the impact of N application timings (from tillering to flag leaf growth stages) on winter wheat grain yield and protein. The study was conducted across 12 site–years in Oklahoma, US. The treatments included a non-fertilized check, a pre-plant application of 100 kg N ha−1, and ten in-season application timings at 100 kg of N ha−1. The in-season treatment applications were initiated at the point when an N deficiency was visually identified by comparing the pre-plant treatment to the non-fertilized check. The treatments were applied in a progressive order every seven growing degree days (GDD > 0 °C) until a cumulative GDD of 63 was reached after visual deficiency (DAVD). All in-season treatments increased grain yield and protein as compared to the non-fertilized check, showing that N was a yield-limiting factor. The nitrogen applications made post Feekes 8 decreased grain yield when compared to pre-plant applications. Across this data set, that timing corresponded to a range of 21 to 63DAVD. The results suggested that forgoing N application until the growth stage Feekes 7, even when the visual N deficiency was highly apparent before that stage, had no negative impact on the yield, and it even increased the yield as compared to the pre-plant application in some cases. The plant developmental stage at which the N application takes place is more critical than the level of N deficiency. Our results show that N fertilizer applications should be made posteriorly to the crop dormancy to maximize both yield and protein, and that plants can recover from N deficiency when applications are made until the late-vegetative phase (Feekes 7). This document shows that winter wheat producers have a much wider N application window than traditionally believed.

Estimation of Maize Foliar Temperature and Stomatal Conductance as Indicators of Water Stress Based on Optical and Thermal Imagery Acquired Using an Unmanned Aerial Vehicle (UAV) Platform

Climatic variability and extreme weather events impact agricultural production, especially in sub-Saharan smallholder cropping systems, which are commonly rainfed. Hence, the development of early warning systems regarding moisture availability can facilitate planning, mitigate losses and optimise yields through moisture augmentation. Precision agricultural practices, facilitated by unmanned aerial vehicles (UAVs) with very high-resolution cameras, are useful for monitoring farm-scale dynamics at near-real-time and have become an important agricultural management tool. Considering these developments, we evaluated the utility of optical and thermal infrared UAV imagery, in combination with a random forest machine-learning algorithm, to estimate the maize foliar temperature and stomatal conductance as indicators of potential crop water stress and moisture content over the entire phenological cycle. The results illustrated that the thermal infrared waveband was the most influential variable during vegetative growth stages, whereas the red-edge and near-infrared derived vegetation indices were fundamental during the reproductive growth stages for both temperature and stomatal conductance. The results also suggested mild water stress during vegetative growth stages and after a hailstorm during the mid-reproductive stage. Furthermore, the random forest model optimally estimated the maize crop temperature and stomatal conductance over the various phenological stages. Specifically, maize foliar temperature was best predicted during the mid-vegetative growth stage and stomatal conductance was best predicted during the early reproductive growth stage. Resultant maps of the modelled maize growth stages captured the spatial heterogeneity of maize foliar temperature and stomatal conductance within the maize field. Overall, the findings of the study demonstrated that the use of UAV optical and thermal imagery, in concert with prediction-based machine learning, is a useful tool, available to smallholder farmers to help them make informed management decisions that include the optimal implementation of irrigation schedules.

Predicting the Chlorophyll Content of Maize over Phenotyping as a Proxy for Crop Health in Smallholder Farming Systems

Smallholder farmers depend on healthy and productive crop yields to sustain their socio-economic status and ensure livelihood security. Advances in South African precision agriculture in the form of unmanned aerial vehicles (UAVs) provide spatially explicit near-real-time information that can be used to assess crop dynamics and inform smallholder farmers. The use of UAVs with remote-sensing techniques allows for the acquisition of high spatial resolution data at various spatio-temporal planes, which is particularly useful at the scale of fields and farms. Specifically, crop chlorophyll content is assessed as it is one of the best known and reliable indicators of crop health, due to its biophysical pigment and biochemical processes that indicate plant productivity. In this regard, the study evaluated the utility of multispectral UAV imagery using the random forest machine learning algorithm to estimate the chlorophyll content of maize through the various growth stages. The results showed that the near-infrared and red-edge wavelength bands and vegetation indices derived from these wavelengths were essential for estimating chlorophyll content during the phenotyping of maize. Furthermore, the random forest model optimally estimated the chlorophyll content of maize over the various phenological stages. Particularly, maize chlorophyll was best predicted during the early reproductive, late vegetative, and early vegetative growth stages to RMSE accuracies of 40.4 µmol/m−2, 39 µmol/m−2, and 61.6 µmol/m−2, respectively. The least accurate chlorophyll content results were predicted during the mid-reproductive and late reproductive growth stages to RMSE accuracies of 66.6 µmol/m−2 and 69.6 µmol/m−2, respectively, as a consequence of a hailstorm. A resultant chlorophyll variation map of the maize growth stages captured the spatial heterogeneity of chlorophyll within the maize field. Therefore, the study’s findings demonstrate that the use of remotely sensed UAV imagery with a robust machine algorithm is a critical tool to support the decision-making and management in smallholder farms.

Spikelet abortion in six-rowed barley is mainly influenced by final spikelet number, with potential spikelet number acting as a suppressor trait.

The potential to increase barley grain yield lies in the indeterminate nature of its inflorescence meristem, which produces spikelets, the basic reproductive unit in grasses that are linked to reproductive success. During early reproductive growth, barley spikes pass through the maximum yield potential-a stage after which no new spikelet ridges are produced. Subsequently, spikelet abortion (SA), a phenomenon in which spikelets abort during spike growth, imposes a bottleneck for increasing the grain yield potential. Here, we studied the potential of main culm spikes by counting potential spikelet number (PSN) and final spikelet number (FSN), and computed the corresponding SA (%) in a panel of 417 six-rowed spring barleys. Our phenotypic data analyses showed a significantly large within- and across-years genotypic variation with high broad-sense heritability estimates for all the investigated traits, including SA. Asian accessions displayed the lowest SA, indicating the presence of favourable alleles that may be exploited in breeding programs. A significantly negative Pearson's product-moment correlation was observed between FSN and SA. Our path analysis revealed that PSN and FSN explain 93% of the observed phenotypic variability for SA, with PSN behaving as a suppressor trait that magnifies the effect of FSN. Based on a large set of diverse barley accessions, our results provide a deeper phenotypic understanding of the quantitative genetic nature of SA, its association with traits of high agronomic importance, and a resource for further genetic analyses.

Potassium Application Rates for Tomato Grown in Soilless Culture under Hot and Humid Greenhouse Conditions

Soilless culture is the most popular cultivation method in greenhouse farming and fertigation in soilless culture determine s the qualitative and quantitatively yield of tomato (Solanum lycopersicum L.). The demand for plant nutrients vary with the surrounding environment, growing medium, and plant growth stages of the crops. Most recommended fertilizer dosages for greenhouse crops have been developed to match with the environmental conditions in the temperate region. This study attempted to identify the optimum potassium (K) fertilizer application rates for different growth stages of tomato grown in soilless culture under semi intensive greenhouse environment (31±2°C daytime temperature and 75% relative humidity). Selected dosages of K for greenhouse tomato (K treatments) were compared at advancing growth stages (0.05-0.4, 0.1-0.55, 0.2-0.6 and 0.25-0.65 g/plant/day) in a replicated trial. At the end of each growth stage, plant growth parameters and leaf tissue nutrients were assessed to compare the K application rates. Based on growth parameters, marketable yield and plant nutrient contents, the optimum K fertilizer application rates for vegetative, early reproductive, middle reproductive and late reproductive growth stages of tomato were 0.1, 0.2, 0.3 and 0.35 g/plant/da y, respectively. These application rates were able to maintain the plant tissue K concentrations of 2.5, 2.1, 2.5 and 2.8% at respective growth stages. Optimum K application rates identified in this study would be useful for making growth specific fertigation recommendations for greenhouse tomato grown in soilless culture under tropical conditions in order to make protected culture is more cost effective and environmental friendly.

Transcriptome Reveals Allele Contribution to Heterosis in Maize.

Heterosis, which has greatly increased maize yields, is associated with gene expression patterns during key developmental stages that enhance hybrid phenotypes relative to parental phenotypes. Before heterosis can be more effectively used for crop improvement, hybrid maize developmental gene expression patterns must be better understood. Here, six maize hybrids, including the popular hybrid Zhengdan958 (ZC) from China, were studied. Maize hybrids created in-house were generated using an incomplete diallel cross (NCII)-based strategy from four elite inbred parental lines. Differential gene expression (DEG) profiles corresponding to three developmental stages revealed that hybrid partial expression patterns exhibited complementarity of expression of certain parental genes, with parental allelic expression patterns varying both qualitatively and quantitatively in hybrids. Single-parent expression (SPE) and parent-specific expression (PSE) types of qualitative variation were most prevalent, 43.73 and 41.07% of variation, respectively. Meanwhile, negative super-dominance (NSD) and positive super-dominance (PSD) types of quantitative variation were most prevalent, 31.06 and 24.30% of variation, respectively. During the early reproductive growth stage, the gene expression pattern differed markedly from other developmental stage patterns, with allelic expression patterns during seed development skewed toward low-value parental alleles in hybrid seeds exhibiting significant quantitative variation-associated superiority. Comparisons of qualitative gene expression variation rates between ZC and other hybrids revealed proportions of SPE-DEGs (41.36%) in ZC seed DEGs that significantly exceeded the average proportion of SPE-DEGs found in seeds of other hybrids (28.36%). Importantly, quantitative gene expression variation rate comparisons between ZC and hybrids, except for transgressive expression, revealed that the ZC rate exceeded the average rate for other hybrids, highlighting the importance of partial gene expression in heterosis. Moreover, enriched ZC DEGs exhibiting distinct tissue-specific expression patterns belonged to four biological pathways, including photosynthesis, plant hormone signal transduction, biology metabolism and biosynthesis. These results provide valuable technical insights for creating hybrids exhibiting strong heterosis.

Environmental Factors Associated With Nitrogen Fixation Prediction in Soybean.

Biological nitrogen (N)-fixation is the most important source of N for soybean [Glycine max (L.) Merr.], with considerable implications for sustainable intensification. Therefore, this study aimed to investigate the relevance of environmental factors driving N-fixation and to develop predictive models defining the role of N-fixation for improved productivity and increased seed protein concentration. Using the elastic net regularization of multiple linear regression, we analyzed 40 environmental factors related to weather, soil, and crop management. We selected the most important factors associated with the relative abundance of ureides (RAU) as an indicator of the fraction of N derived from N-fixation. The most relevant RAU predictors were N fertilization, atmospheric vapor pressure deficit (VPD) and precipitation during early reproductive growth (R1–R4 stages), sowing date, drought stress during seed filling (R5–R6), soil cation exchange capacity (CEC), and soil sulfate concentration before sowing. Soybean N-fixation ranged from 60 to 98% across locations and years (n = 95). The predictive model for RAU showed relative mean square error (RRMSE) of 4.5% and an R2 value of 0.69, estimated via cross-validation. In addition, we built similar predictive models of yield and seed protein to assess the association of RAU and these plant traits. The variable RAU was selected as a covariable for the models predicting yield and seed protein, but with a small magnitude relative to the sowing date for yield or soil sulfate for protein. The early-reproductive period VPD affected all independent variables, namely RAU, yield, and seed protein. The elastic net algorithm successfully depicted some otherwise challenging empirical relationships to assess with bivariate associations in observational data. This approach provides inference about environmental variables while predicting N-fixation. The outcomes of this study will provide a foundation for improving the understanding of N-fixation within the context of sustainable intensification of soybean production.

Rice response to sublethal rates of paraquat, metribuzin, fomesafen, and cloransulam-methyl at different application timings

AbstractThe application of paraquat mixtures with residual herbicides before planting rice is a common treatment in Mississippi, and rice in proximity is susceptible to off-target movement of these applications. Four concurrent studies were conducted in Stoneville, MS, to characterize rice performance following exposure to a sublethal rate of paraquat, metribuzin, fomesafen, and cloransulam-methyl at different application timings. Herbicides were applied to rice at the growth stages of spiking to one-leaf (VEPOST), two- to three-leaf (EPOST), three- to four-leaf (MPOST), 7 d postflood (PFLD), and panicle differentiation (PD). Regardless of application timing, rice injury following exposure to paraquat was ≥45%. Delays in maturity were increased by 0.3 d d−1 following paraquat from emergence through PD. Dry weight, rough rice yield, panicle density, and germination were reduced by 18.7 g, 131.5 kg ha−1, 5.6 m−2, and 0.3%, respectively, per day from application of paraquat at emergence through PD. By 28 d after treatment (DAT), metribuzin injured rice 3% to 6%, and that injury did not translate into a yield reduction. Regardless of application timing, rice injury following fomesafen application ranged from 2% to 5% 28 DAT. Rice exposed to cloransulam-methyl EPOST exhibited the greatest root and foliar injury 21 DAT and 28 DAT, respectively. Additionally, when rice was exposed to cloransulam-methyl EPOST, yield was reduced to 6,540 kg ha−1 compared with a yield of 7,850 kg ha−1 from nontreated rice. Rice yield was negatively affected after paraquat was applied any time after rice emergence. However, applications of paraquat to rice at early reproductive growth stages reduced rough rice yield and seed germination the greatest. Application timing is crucial in determining severity of rice injury. Early-season injury to rice following paraquat application had less effect on yield compared with injury at later stages. Additionally, fields devoted to seed rice production are at risk for reduced seed germination if they are exposed to paraquat during early reproductive growth stages.

Yield estimation of soybean breeding lines under drought stress using unmanned aerial vehicle-based imagery and convolutional neural network

Crop yield is a primary trait to select superior genotypes and evaluate breeding efficiency in breeding programs. Crops with high yield potential are usually selected from numerous breeding lines in multiple years and locations. However, the efficiency of conventional breeding programs is limited by the capacity of field phenotyping, which can be improved by developing high-throughput field phenotyping systems using emerging technologies, including Unmanned Aerial Vehicle (UAV) imagery and deep learning technologies. The goal of this study was to evaluate the performance of a UAV imaging system and convolutional neural network (CNN) in estimating yield of soybean breeding lines. In this study, 972 soybean breeding lines in three maturity groups were planted under rainfed conditions for testing their drought tolerance. Aerial images were taken at the late vegetation (V6), early (R1), and late reproductive (R6-R8) growth stages. Seven image features associated with plant height, canopy colour, and canopy texture were selected to estimate the yield of each breeding line. A mixed CNN model was built to estimate soybean yield by taking the seven image features and two categorical factors, i.e. maturity group and drought tolerance, as predictors. Results show that image features collected at the early and late reproductive growth stages are comparably promising in estimating soybean yield. The prediction model could explain 78% of the measured yield with a root mean square error of 391.0 kg·ha−1 (33.8% to the average yield), indicating that the UAV imagery and deep learning models are promising in estimating yield for soybean breeding purposes.

AtFLL2, a member of the FLO2 gene family, affects the enlargement of leaves at the vegetative stage and facilitates the regulation of carbon metabolism and flow.

Arabidopsis thaliana FLL2, a member of the FLO2 gene family, is expressed specifically in green leaves. The fll2 mutant showed significantly large rosette leaves and reduced the chlorophyll content. The sucrose content was significantly reduced. The glucose content was higher during the vegetative growth stage but decreased during the early reproductive growth stage. The amount of assimilated starch was lower than that in the wild type plant. The expression levels of genes involved in biosynthesis of sucrose and starch were largely altered. These results suggest that, in the fll2 mutant, a small amount of photosynthetic products was used for the biosynthesis of starch, and the products were supplied to promote intracellular growth of the source organs or for transport to the sink organs. These findings suggest that FLL2 is a factor affecting the expression level of genes involved in sugar metabolism, whose mutation caused a change in the assimilated products. Abbreviations : DAS: days after sowing.

Canopy greenness as a midseason nitrogen management tool in corn production

AbstractAn in‐season nitrogen (N) assessment tool would be valuable in improving N management in corn (Zea mays L.). This study evaluated the intensity of canopy greenness, as determined by the Dark Green Color Index (DGCI) measured from aerial digital images, to serve as a guideline for in‐season N management. Measurements of DGCI from high N (258 kg N ha−1) experimental units were used as a reference DGCI (RDGCI) to correct for variable lighting conditions. Ability of DGCI to assess in‐season N status was investigated by predicting leaf N (LN) concentration and relative grain yield (RGY) as functions of DGCI and RDGCI. Eight field studies were conducted on silt loam soils in Arkansas. Digital images and leaf samples were collected at the 10th collared leaf stage (V10), the 12th collared leaf stage (V12), and tasseling (VT). Across a wide range of environmental lighting conditions, LN was described by a regression (P < .0001) as a function of DGCI and RDGCI with a root mean square error of 3.49 g N kg−1 and a bias of −0.03 g N kg−1. Likewise, RGY was regressed against DGCI and RDGCI (P < .0001) and predicted RGY with a RMSE of 10.3% and a bias of −0.3%. Measurements of DGCI and RDGCI predicted LN and RGY accurately, demonstrating the potential to assess N status and improve N management in corn. These relationships provide reliable metrics for N status during late vegetative and early reproductive growth using aerial images.

First versus last born: Flowers, pods, and yield formation in no‐tillage lentil

AbstractLentil (Lens culinaris Medik.) is a grain legume crop grown under no‐tillage (NT) management and rhizobia inoculation in the northern Great Plains. Our goal was to characterize pod growth characteristics to understand yield formation in North American short‐season cultivars. Objectives were to compare pods on nodes during early and late reproductive growth through flower and pod number, pod setting percentage, pod growth rate, effective pod‐filling period, and final pod size, and to test if N fertility could increase pod numbers and pod growth under NT management. Two field experiments, Study 1 and Study 2, were conducted at Saskatoon and Indian Head, SK, in western Canada. For Study 1, cultivar CDC Sedley was treated with 0–60 kg N ha−1 on long‐term NT fields for 3 yr (2006–2008). For Study 2, eight cultivars from three maturity classes were grown under control, inoculant, and N fertilizer regimes under NT at Saskatoon (2006 and 2007). Lentil set more yield in early‐flowering nodes via flower number, with 2.6 flowers node−1. Late nodes had fewer (2.1) flowers but the same pod setting percentage of 80% for CDC Sedley, and a lower percentage across eight cultivars. Late nodes produced pods with greater growth rates and shorter filling times, resulting in fewer but similar sized pods. The largest pods were found on nodes with fewer flowers and when moisture was adequate during filling. Drought stress in 2007 occurred and lowered pod setting to 60% and shortened the effective pod‐filling period during late reproductive drought. During the two wet years, higher N rates increased late‐season pod growth rate, but not flower numbers. An increase in late node and flower production may further improve yield, particularly in large‐seeded cultivars.

Economic injury levels for pea aphids (Hemiptera: Aphididae) as direct pests of commercial dry peas (Fabaceae) during reproductive growth stages in the Pacific Northwest of North America

AbstractEmpirically-based economic injury levels are lacking for pea aphid, Acyrthosiphon pisum Harris (Hemiptera: Aphididae), as a direct pest of dry peas, Pisum sativum Linnaeus (Fabaceae). To address this need, the relationship between pea aphid density and yield of dry pea (cultivar Aragorn) were quantified by encaging pea aphids at varying densities for 17-day infestation periods during 2009 and 2010 in Moscow, Idaho, United States of America. Pea aphid density after infestation at the early reproductive stage of the crop (x) significantly reduced dry pea seed yield (relative weight of US #1 dry peas, y): y = 0.7733 − 0.00998x + 0.000037x2. Economic injury levels were computed based on this relationship and incorporating the cost of control, crop market value, insecticide efficacy, and crop yield potential. The resulting economic injury levels ranged from five to 19 pea aphids per plant at the start of early reproductive growth stages of dry peas. For usability these were converted to sweep net sample size equivalents of 86–307 pea aphids per twenty-five 180-degree sweeps with a standard sweep net. These economic injury levels are applicable in the inland Pacific Northwest, United States of America, where they were developed and likely in other regions with similar climatic and agronomic conditions.

Comparison of Yield Components and Physiological Parameters of Drought Tolerant and Conventional Corn Hybrids

Core Ideas Drought‐tolerant corn hybrids did not out‐yield conventional corn hybrids. No yield components differed among hybrids. Corn (Zea mays L.) consumes large quantities of water to produce yield. When sufficient hectares within an area are irrigated using deep wells, corn production can contribute to depleting underground aquafers. Hybrids genetically engineered for drought tolerance have recently become available to producers. Six hybrids, three sold as drought tolerant, were evaluated for yield, yield components, and physiological parameters of net assimilation rate (A), stomatal conductance (gs), transpiration rate (Em), intercellular CO2 (Ci), and intrinsic water use efficiency (IWUE) during early reproductive growth in 2014 through 2016 with irrigated and nonirrigated treatments applied after anthesis. Irrigation did not improve yields among hybrids in any year. Hybrid differences in yield occurred each year but with no consistency across years, and genetically drought‐tolerant hybrids were not always superior. Mean yields by year were 2016 (12,590 kg ha−1) > 2015 (10,214 kg ha−1) > 2014 (7843 kg ha−1). Low yields in 2014 are attributed to fewer kernels per plant due to possible Formesafen herbicide carryover applied to soybean (Glycine max L. Merr.) the previous year. Of the physiological parameters measured (A, gs, Em, Ci, and IWUE), no hybrid differences were observed, only Em was found to be greater at anthesis (R1) than at kernel filling (R2), and irrigated treatments had greater Em than nonirrigated treatments. Data from this experiment and its environmental conditions showed no yield or physiological differences between drought‐tolerant and conventional corn hybrids. Only on nonirrigated fields during limited soil moisture would drought‐tolerant hybrids possibly be beneficial. Any reduction in irrigation by growing drought‐resistant hybrids could not be determined in this experiment.

Impact of Late‐Season Herbicide Applications on Winter Canola Yield and Seed Quality

Core Ideas The application of glyphosate during early reproductive growth resulted in significant declines in yield. Decreased yields with late‐season herbicides were associated with an increased number of aborted pods; however, this may not be the only factor contributing to the yield decline. Seed oil content followed the same general trend as grain yield, with decreased oil yields when glyphosate was applied during early reproductive growth. In canola (Brassica napus L.), control of grassy weeds can typically be achieved in the fall or spring with timely herbicide applications. However, environmental conditions can delay these applications into canola reproductive growth stages, which have an unknown effect on canola productivity. Therefore, a study was conducted to determine the impact of late‐season herbicide applications on canola grain yields and seed quality. The impact of three herbicides (glyphosate [N‐(phosphonomethyl)glycine], clethodim [(E)‐2‐[1‐[[3‐chloro‐2‐propenyl)oxy]imino]propyl]5=[2‐(ethylthio)propyl]‐2‐hydroxy‐2‐cyclohexen‐1‐one], and quizalofop‐P‐ethyl [Ethyl(R)‐2‐[4‐(6‐chloroquinoxalin‐2‐yloxy)‐phenoxy]propionate]) and three applications timings (bolting, early flower, and mid‐flower) were investigated on winter canola from 2015 through 2018 in Perkins and Stillwater, OK. Grain yields and pod abortion were collected at maturity. Subsamples of harvested seed were collected from each plot and used for oil analysis. When averaged across years, the application of glyphosate at bolting and early flower resulted in yield decreases of 531 and 488 kg ha–1 compared with the nontreated control. Yield following the application of clethodim at early flower resulted in a 185 kg ha–1 (2017) and 280 (2018) kg ha–1 decrease in yields compared with the control. Although yield decreases were observed following a few treatments, a significant increase in pod abortion was noted following many herbicide treatments, indicating that pod abortion was not the only factor contributing to yield declines. Decreases in oil content were only found following glyphosate applications at bolting and early flower. These results indicate that late‐season herbicide applications, especially glyphosate, should be avoided in winter canola.

Effects of Shading Stress on Grain Number, Yield, and Photosynthesis during Early Reproductive Growth in Wheat

Climate change causes shading and threatens wheat (Triticum aestivum L.) yield. Understanding the effects of shading on grain number and morphological characteristics of winter wheat during the young microspore (YM) stage may secure grain yield. Field and pot experiments were performed with six widely planted winter wheat cultivars in the North China Plain (NCP), subjected to 98% blocking of natural light for different shading durations (0, 1, 2, 3, 4, 5, and 6 d) during the YM stage. Results showed that the YM stage was highly sensitive to shading stress, which significantly reduced grain number, causing grain yield loss. Moreover, response to shading varied among cultivars. Sensitive cultivars, such as Kenong9204 and Liangxing99, showed decreases in grain yield per plant of 32.2 to 74.9% and 25.7 to 91.4% in 2016 and 23.0 to 89.2% and 21.9 to 94.3% in 2017 with increasing shading duration. Reduction in grain yield with increasing shading duration was associated with decreasing grain number, grain weight, plant height, and dry matter weight of main stems and tillers. Tolerant cultivars Jimai32 and Henong825 experienced decreases in grain yield per plant of 8.2 to 17.3% and 11.3 to 14.2% in 2016 and 9.7 to 23.7% and 6.5 to 18.5% in 2017 with increasing shading duration. Adaptation of tolerant cultivars to shading was enhanced by rapid tiller development and photosynthesis recovery, inducing productive tillers to increase grain number per tiller, thus resulting in a smaller reduction in grain yield. Therefore, selecting shade‐tolerant cultivars may help to ensure high yield in winter wheat in fog‐haze areas of the NCP.

Response of Non–Dicamba-Resistant Soybean to Dicamba As Influenced by Growth Stage and Herbicide Rate

AbstractIntroduction of the Roundup Ready® Xtend system (Monsanto Co., St. Louis, MO) provides an alternative weed management option for growers, but of concern is the risk of dicamba injury to sensitive crops, particularly soybean from off-target movement and tank contamination. Experiments were conducted to determine the response of soybean to low rates of dicamba over a wide range of application timings. Two glufosinate-resistant varieties (HBK 4950LL–indeterminate and HALO 5.45LL–determinate) commonly grown in Arkansas were chosen for these studies. Two rates of dicamba, 2.18 and 8.75 g ae ha–1(1/256× and 1/64× of the POST labeled rate for dicamba-resistant soybean), were applied at two vegetative (V4, V6) and six reproductive (R1 to R6) growth stages. Compared to the nontreated control, dicamba applied during late vegetative and early reproductive growth of soybean caused leaf injury, plant height reduction, and seed yield loss for both soybean cultivars. Averaged across dicamba rates applied at R1, soybean seed yield was reduced 14% for the HBK 4950LL cultivar and 19% for the HALO 5.45LL cultivar. Averaged over rates, dicamba applied at R1 to the HALO 5.45LL and HBK 4950LL soybean resulted in 48% and 43% visible injury 4 wk after treatment, respectively. Grain yield was similar to that of the nontreated control when dicamba was applied at the later reproductive stages averaged across rates.