Neonicotinoids are the most widely used insecticides worldwide and are typically deployed as seed treatments (hereafter NST) in many grain and oilseed crops, including soybeans. However, there is a ...surprising dearth of information regarding NST effectiveness in increasing soybean seed yield, and most published data suggest weak, or inconsistent yield benefit. The US is the key soybean-producing nation worldwide and this work includes soybean yield data from 194 randomized and replicated field studies conducted specifically to evaluate the effect of NSTs on soybean seed yield at sites within 14 states from 2006 through 2017. Here we show that across the principal soybean-growing region of the country, there are negligible and management-specific yield benefits attributed to NSTs. Across the entire region, the maximum observed yield benefits due to fungicide (FST = fungicide seed treatment) + neonicotinoid use (FST + NST) reached 0.13 Mg/ha. Across the entire region, combinations of management practices affected the effectiveness of FST + NST to increase yield but benefits were minimal ranging between 0.01 to 0.22 Mg/ha. Despite widespread use, this practice appears to have little benefit for most of soybean producers; across the entire region, a partial economic analysis further showed inconsistent evidence of a break-even cost of FST or FST + NST. These results demonstrate that the current widespread prophylactic use of NST in the key soybean-producing areas of the US should be re-evaluated by producers and regulators alike.
Crop yield prediction which provides critical information for management decision-making is of significant importance in precision agriculture. Traditional manual inspection and calculation are often ...laborious and time-consuming. For yield prediction using high-resolution images, existing methods, e.g., convolutional neural network, are challenging to model long range multi-level dependencies across image regions. This paper proposes a transformer-based approach for yield prediction using early-stage images and seed information. First, each original image is segmented into plant and soil categories. Two vision transformer (ViT) modules are designed to extract features from each category. Then a transformer module is established to deal with the time-series features. Finally, the image features and seed features are combined to estimate the yield. A case study has been conducted using a dataset that was collected during the 2020 soybean-growing seasons in Canadian fields. Compared with other baseline models, the proposed method can reduce the prediction error by more than 40%. The impact of seed information on predictions is studied both between models and within a single model. The results show that the influence of seed information varies among different plots but it is particularly important for the prediction of low yields.
In general, early detection and timely management of plant diseases are essential for reducing yield loss. Traditional manual inspection of fields is often time-consuming and laborious. Automated ...imaging techniques have recently been successfully applied to detect plant diseases. However, these methods mostly focus on the current state of the crop. This paper proposes a gated recurrent unit (GRU)-based model to predict soybean sudden death syndrome (SDS) disease development. To detect SDS at a quadrat level, the proposed method uses satellite images collected from PlanetScope as the training set. The pixel image data include the spectral bands of red, green, blue and near-infrared (NIR). Data collected during the 2016 and 2017 soybean-growing seasons were analyzed. Instead of using individual static imagery, the GRU-based model converts the original imagery into time-series data. SDS predictions were made on different data scenarios and the results were compared with fully connected deep neural network (FCDNN) and XGBoost methods. The overall test accuracy of classifying healthy and diseased quadrates in all methods was above 76%. The test accuracy of the FCDNN and XGBoost were 76.3–85.5% and 80.6–89.2%, respectively, while the test accuracy of the GRU-based model was 82.5–90.4%. The calculation results show that the proposed method can improve the detection accuracy by up to 7% with time-series imagery. Thus, the proposed method has the potential to predict SDS at a future time.
The NF-Y gene family is a highly conserved set of transcription factors. The functional transcription factor complex is made up of a trimer between NF-YA, NF-YB, and NF-YC proteins. While mammals ...typically have one gene for each subunit, plants often have multigene families for each subunit which contributes to a wide variety of combinations and functions. Soybean plants with an overexpression of a particular NF-YC isoform
(Glyma.04g196200) in soybean cultivar Williams 82, had a lower amount of starch in its leaves, a higher amount of protein in its seeds, and increased broad disease resistance for bacterial, viral, and fungal infections in the field, similar to the effects of overexpression of its isoform
(Glyma.06g169600). Interestingly,
(overexpression) plants also filled pods and senesced earlier, a novel trait not found in
plants. No yield difference was observed in
compared with the wild-type control. Sequence alignment of GmNF-YC4-2, GmNF-YC4-1 and AtNF-YC1 indicated that faster maturation may be a result of minor sequence differences in the terminal ends of the protein compared to the closely related isoforms.
Seed treatments for the management of sudden death syndrome (SDS) caused by
are available in the United States and Canada; however, side-by-side comparisons of these seed treatments are lacking. ...Sixteen field experiments were established in Illinois, Indiana, Iowa, Michigan, and Wisconsin, United States, and Ontario, Canada, in 2019 and 2020 to evaluate seed treatment combinations. Treatments included a nontreated check (NTC), fungicide and insecticide base seed treatments (base), fluopyram, base + fluopyram, base + saponin extracts from
, base + fluopyram + heat-killed
, base + pydiflumetofen, base + thiabendazole + heat-killed
, and base + thiabendazole +
extracts
heat-killed
. Treatments were tested on SDS moderately resistant and susceptible soybean cultivars at each location. Overall, NTC and base had the most root rot, most foliar disease index (FDX), and lowest yield. Base + fluopyram and base + pydiflumetofen were most effective for managing SDS. Moderately resistant cultivars reduced FDX in both years but visual root rot was greater on the moderately resistant than the susceptible cultivars in 2020. Yield response to cultivar was also inconsistent between the 2 years. In 2020, the susceptible cultivar provided significantly more yield than the moderately resistant cultivar. Treatment effect for root rot and FDX was similar in field and greenhouse evaluations. These results reinforce the need to include root rot evaluations in addition to foliar disease evaluations in the breeding process for resistance to
and highlights the importance of an integrated SDS management plan because not a single management tactic alone provides adequate control of the disease.
Frogeye leaf spot, caused by Cercospora sojina K. Hara, is a major soybean (Glycine max L. Merr.) disease that has become more prevalent in the upper Midwest and can be managed with foliar ...fungicides. Incorporating disease severity into a parameter directly related to yield may better relay the impact of disease on yield and yield components than severity alone. Experiments during the 2018 and 2019 growing seasons in fields located in north central and southwestern Iowa were completed to (i) determine how foliar fungicides affected frogeye leaf spot, remotely sensed plant health indicators, and soybean yield, and (ii) compare the relationship and impact of foliar fungicides and frogeye leaf spot on radiation-use efficiency (RUE) estimated using unmanned aerial vehicle reflectance data. Fungicides affected frogeye severity and yield in one of the three locations; in Lewis 2018, the flutriafol + fluoxastrobin treatment reduced frogeye leaf spot severity by over 50% and increased yield by 19% compared to non-treated controls. Applications of foliar fungicides increased canopy coverage compared to non-treated controls (p = 0.012), but NDVI, SPAD values, and RUE values did not differ between fungicide treatments at all three locations. Estimated soybean RUE values (1.05 to 1.66 g Mj−1) were within the range of known values. Overall, this study indicates that RUE can be a valuable resource to estimate the impact of the disease on yield, however, additional research will be needed to use RUE within certain pathosystems.
Foliar fungicide applications to corn (Zea mays L.) occur at one or more application timings ranging from early vegetative growth stages to mid-reproductive stages. Previous studies indicated that ...fungicide applications are profitable under high disease pressure when applied during the tasseling to silking growth stages. Few comprehensive studies in corn have examined the impact of fungicide applications at an early vegetative growth stage (V6) compared to late application timings (VT) for yield response and return on fungicide investment (ROI) across multiple locations.
Compare yield response of fungicide application timing across multiple fungicide classes and calculate the probability of positive ROI.
Data were collected specifically for this analysis using a uniform protocol conducted in 13 states in the United States and one province in Canada from 2014-2015. Data were subjected to a primary mixed-model analysis of variance. Subsequent univariate meta-analyses, with and without moderator variables, were performed using standard meta-analytic procedures. Follow-up power and prediction analyses were performed to aid interpretation and development of management recommendations.
Fungicide application resulted in a range of yield responses from -2,683.0 to 3,230.9 kg/ha relative to the non-treated control, with 68.2% of these responses being positive. Evidence suggests that all three moderator variables tested (application timing, fungicide class, and disease base level), had some effect (α = 0.05) on the absolute difference in yield between fungicide treated and non-treated plots (Formula: see text). Application timing influenced Formula: see text, with V6 + VT and the VT application timings resulting in greater yield responses than the V6 application timing alone. Fungicide formulations that combined demethylation inhibitor and quinone outside inhibitor fungicides significantly increased yield response.
Foliar fungicide applications can increase corn grain yield. To ensure the likelihood of a positive ROI, farmers should focus on applications at VT and use fungicides that include a mix of demethylation inhibitor and quinone outside inhibitor active ingredients.
Summary
Enhancing the nutritional quality and disease resistance of crops without sacrificing productivity is a key issue for developing varieties that are valuable to farmers and for simultaneously ...improving food security and sustainability. Expression of the Arabidopsis thaliana species‐specific AtQQS (Qua‐Quine Starch) orphan gene or its interactor, NF‐YC4 (Nuclear Factor Y, subunit C4), has been shown to increase levels of leaf/seed protein without affecting the growth and yield of agronomic species. Here, we demonstrate that overexpression of AtQQS and NF‐YC4 in Arabidopsis and soybean enhances resistance/reduces susceptibility to viruses, bacteria, fungi, aphids and soybean cyst nematodes. A series of Arabidopsis mutants in starch metabolism were used to explore the relationships between QQS expression, carbon and nitrogen partitioning, and defense. The enhanced basal defenses mediated by QQS were independent of changes in protein/carbohydrate composition of the plants. We demonstrate that either AtQQS or NF‐YC4 overexpression in Arabidopsis and in soybean reduces susceptibility of these plants to pathogens/pests. Transgenic soybean lines overexpressing NF‐YC4 produce seeds with increased protein while maintaining healthy growth. Pull‐down studies reveal that QQS interacts with human NF‐YC, as well as with Arabidopsis NF‐YC4, and indicate two QQS binding sites near the NF‐YC‐histone‐binding domain. A new model for QQS interaction with NF‐YC is speculated. Our findings illustrate the potential of QQS and NF‐YC4 to increase protein and improve defensive traits in crops, overcoming the normal growth‐defense trade‐offs.
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