Filling the agronomic data gap through a minimum data collection approach.

Context: Agronomic data such as applied inputs, management practices, and crop yields are needed for assessing productivity, nutrient balances, resource use efficiency, as well as other aspects of environmental and economic performance of cropping systems. In many instances, however, these data are only available at a coarse level of aggregation or simply do not exist. Objectives: Here we developed an approach that identifies sites for agronomic data collection for a given crop and country, seeking a balance between minimizing data collection efforts and proper representation of the main crop producing areas. Methods: The developed approach followed a stratified sampling method based on a spatial framework that delineates major climate zones and crop area distribution maps, which guides selection of sampling areas (SA) until half of the national harvested area is covered. We provided proof of concept about the robustness of the approach using three rich databases including data on fertilizer application rates for maize, wheat, and soybean in Argentina, soybean in the USA, and maize in Kenya, which were collected via local experts (Argentina) and field surveys (USA and Kenya). For validation purposes, fertilizer rates per crop and nutrient derived at (sub-) national level following our approach were compared against those derived using all data collected from the whole country. Results: Application of the approach in Argentina, USA, and Kenya resulted in selection of 12, 28, and 10 SAs, respectively. For each SA, three experts or 20 fields were sufficient to give a robust estimate of average fertilizer rates applied by farmers. Average rates at national level derived from our approach compared well with those derived using the whole database ( ± 10 kg N, ± 2 kg P, ± 1 kg S, and ± 5 kg K per ha) requiring less than one third of the observations. Conclusions: The developed minimum crop data collection approach can fill the agronomic data gaps in a cost-effective way for major crop systems both in large- and small-scale systems. Significance: The proposed approach is generic enough to be applied to any crop-country combination to guide collection of key agricultural data at national and subnational levels with modest investment especially for countries that do not currently collect data.

Integration of Soybean (Glycine max) Resistance Levels to Sclerotinia Stem Rot into Predictive Sclerotinia sclerotiorum Apothecial Models.

Sclerotinia stem rot (SSR) is a major disease of soybean across the Upper Midwest region of the United States. Management of this disease has relied on fungicide applications, but due to the environmental conditions necessary for SSR to develop, many of these applications are unnecessary. To mitigate this, predictive models have been developed using localized weather data for predicting the formation of Sclerotinia sclerotiorum apothecia, the inoculum source of SSR, and these models were integrated into a decision support system called Sporecaster. However, these models do not account for the soybean resistance levels to SSR. In this study, fungicide trials were performed across seven site-years in Wisconsin between 2020 and 2022 examining fungicide applications applied at one of three action thresholds (low, moderate, and high) following Sporecaster recommendations in combination with four soybean varieties representing three SSR resistance levels (susceptible, moderately resistant, and resistant). From these trials, the low and moderate action thresholds resulted in similarly low disease severity index (DIX) levels comparable to the standard across all varieties. However, the low action threshold was most accurate for predicting SSR development in the susceptible variety, and the high action threshold was most accurate for predicting SSR development for the three resistant varieties. Both the susceptible soybean and a moderately resistant line yielded similarly high results. Additionally, the use of all fungicide applications led to similar partial profits at grain sale prices of either $0.44 or $0.55 kg-1. Overall, this study uncovered relationships between soybean resistance levels to SSR and Sporecaster, allowing for improved recommendations for fungicide applications.

Integration of Row Spacing, Seeding Rates, and Fungicide Applications for Control of Sclerotinia Stem Rot in Glycine max.

Soybean (Glycine max) farmers in the Upper Midwest region of the United States often experience severe yield losses due to Sclerotinia stem rot (SSR). Previous studies have revealed benefits of individual management practices for SSR. This study examined the integration of multiple control practices on the development of SSR, yield, and the economic implications of these practices. Combinations of row spacings, seeding rates, and fungicide applications were examined in multisite field trials across the Upper Midwest from 2017 to 2019. These trials revealed that wide row spacing and low seeding rates individually reduced SSR levels but also reduced yields. Yields were similar across the three highest seeding rates examined. However, site-years where SSR developed showed the highest partial profits at the intermediate seeding rates. This finding indicates that partial profits in diseased fields were reduced by high seeding rates, but this trend was not observed when SSR did not develop. Fungicides strongly reduced the development of SSR while also increasing yields. However, there was a reduction in partial profits due to their use at a low soybean sale price, but at higher sale prices fungicide use was similar to not treating. Additionally, the production of new inoculum was predicted from disease incidence, serving as an indicator of increased risk for SSR development in future years. Overall, this study suggests using wide rows and low seeding rates in fields with a history of SSR while reserving narrow rows and higher seeding rates for fields without a history of SSR.

A machine learning interpretation of the contribution of foliar fungicides to soybean yield in the north-central United States.

Foliar fungicide usage in soybeans in the north-central United States increased steadily over the past two decades. An agronomically-interpretable machine learning framework was used to understand the importance of foliar fungicides relative to other factors associated with realized soybean yields, as reported by growers surveyed from 2014 to 2016. A database of 2738 spatially referenced fields (of which 30% had been sprayed with foliar fungicides) was fit to a random forest model explaining soybean yield. Latitude (a proxy for unmeasured agronomic factors) and sowing date were the two most important factors associated with yield. Foliar fungicides ranked 7th out of 20 factors in terms of relative importance. Pairwise interactions between latitude, sowing date and foliar fungicide use indicated more yield benefit to using foliar fungicides in late-planted fields and in lower latitudes. There was a greater yield response to foliar fungicides in higher-yield environments, but less than a 100 kg/ha yield penalty for not using foliar fungicides in such environments. Except in a few production environments, yield gains due to foliar fungicides sufficiently offset the associated costs of the intervention when soybean prices are near-to-above average but do not negate the importance of disease scouting and fungicide resistance management.

Advancing agricultural research using machine learning algorithms.

Rising global population and climate change realities dictate that agricultural productivity must be accelerated. Results from current traditional research approaches are difficult to extrapolate to all possible fields because they are dependent on specific soil types, weather conditions, and background management combinations that are not applicable nor translatable to all farms. A method that accurately evaluates the effectiveness of infinite cropping system interactions (involving multiple management practices) to increase maize and soybean yield across the US does not exist. Here, we utilize extensive databases and artificial intelligence algorithms and show that complex interactions, which cannot be evaluated in replicated trials, are associated with large crop yield variability and thus, potential for substantial yield increases. Our approach can accelerate agricultural research, identify sustainable practices, and help overcome future food demands.

Host-Induced Gene Silencing of a Sclerotinia sclerotiorum oxaloacetate acetylhydrolase Using Bean Pod Mottle Virus as a Vehicle Reduces Disease on Soybean.

A lack of complete resistance in the current germplasm complicates the management of Sclerotinia stem rot (SSR) caused by Sclerotinia sclerotiorum in soybean. In this study, we used bean pod mottle virus (BPMV) as a vehicle to down-regulate expression of a key enzyme in the production of an important virulence factor in S. sclerotiorum, oxalic acid (OA). Specifically, we targeted a gene encoding oxaloacetate acetylhydrolase (Ssoah1), because Ssoah1 deletion mutants are OA deficient and non-pathogenic on soybean. We first established that S. sclerotiorum can uptake environmental RNAs by monitoring the translocation of Cy3-labeled double-stranded and small interfering RNA (ds/siRNAs) into fungal hyphae using fluorescent confocal microscopy. This translocation led to a significant decrease in Ssoah1 transcript levels in vitro. Inoculation of soybean plants with BPMV vectors targeting Ssoah1 (pBPMV-OA) also led to decreased expression of Ssoah1. Importantly, pBPMV-OA inoculated plants showed enhanced resistance to S. sclerotiorum compared to empty-vector control plants. Our combined results provide evidence supporting the use of HIGS and exogenous applications of ds/siRNAs targeting virulence factors such as OA as viable strategies for the control of SSR in soybean and as discovery tools that can be used to identify previously unknown virulence factors.

Modeling the relationship between estimated fungicide use and disease-associated yield losses of soybean in the United States II: Seed-applied fungicides vs seedling diseases.

Use of seed-applied fungicides has become commonplace in the United States soybean production systems. Although fungicides have the potential to protect seed/seedlings from critical early stage diseases such as damping-off and root/stem rots, results from previous studies are not consistent in terms of seed-applied fungicide's ability to mitigate yield losses. In the current study, the relationship between estimated soybean production losses due to seedling diseases and estimated seed-applied fungicide use was investigated using annual data from 28 soybean growing states in the U.S. over the period of 2006 to 2014. National, regional (northern and southern U.S.), state, and temporal scale trends were explored using mixed effects version of the regression analysis. Mixed modeling allowed computing generalized R2 values for conditional (R2GLMM(c); contains fixed and random effects) and marginal (R2GLMM(m); contains only fixed effects) models. Similar analyses were conducted to investigate how soybean production was related to fungicide use. National and regional scale modeling revealed that R2GLMM(c) values were significantly larger compared to R2GLMM(m) values, meaning fungicide use had limited utility in explaining the national/regional scale variation of yield loss and production. The state scale analysis revealed the usefulness of seed-applied fungicides to mitigate seedling diseases-associated soybean yield losses in Illinois, Indiana, North Carolina, and Ohio. Further, fungicide use positively influenced the soybean production and yield in Illinois and South Dakota. Taken together, use of seed-applied fungicide did not appear to be beneficial to many of the states. Our findings corroborate the observations made by a number of scientists through field scale seed-applied fungicide trials across the U.S and reiterate the importance of need base-use of seed-applied fungicides rather than being a routine practice in soybean production systems.

Modeling the relationship between estimated fungicide use and disease-associated yield losses of soybean in the United States I: Foliar fungicides vs foliar diseases.

Fungicide use in the United States to manage soybean diseases has increased in recent years. The ability of fungicides to reduce disease-associated yield losses varies greatly depending on multiple factors. Nonetheless, historical data are useful to understand the broad sense and long-term trends related to fungicide use practices. In the current study, the relationship between estimated soybean yield losses due to selected foliar diseases and foliar fungicide use was investigated using annual data from 28 soybean growing states over the period of 2005 to 2015. For national and regional (southern and northern United States) scale data, mixed effects modeling was performed considering fungicide use as a fixed and state and year as random factors to generate generalized R2 values for marginal (R2GLMM(m); contains only fixed effects) and conditional (R2GLMM(c); contains fixed and random effects) models. Similar analyses were performed considering soybean production data to see how fungicide use affected production. Analyses at both national and regional scales showed that R2GLMM(m) values were significantly smaller compared to R2GLMM(c) values. The large difference between R2 values for conditional and marginal models indicated that the variation of yield loss as well as production were predominantly explained by the state and year rather than the fungicide use, revealing the general lack of fit between fungicide use and yield loss/production at national and regional scales. Therefore, regression models were fitted across states and years to examine their importance in combination with fungicide use on yield loss or yield. In the majority of cases, the relationship was nonsignificant. However, the relationship between soybean yield and fungicide use was significant and positive for majority of the years in the study. Results suggest that foliar fungicides conferred yield benefits in most of the years in the study. Furthermore, the year-dependent usefulness of foliar fungicides in mitigating soybean yield losses suggested the possible influence of temporally fluctuating abiotic factors on the effectiveness of foliar fungicides and/or target disease occurrence and associated loss magnitudes.

Neonicotinoid seed treatments of soybean provide negligible benefits to US farmers.

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.

Meta-Analytic and Economic Approaches for Evaluation of Pesticide Impact on Sclerotinia Stem Rot Control and Soybean Yield in the North Central United States.

As complete host resistance in soybean has not been achieved, Sclerotinia stem rot (SSR) caused by Sclerotinia sclerotiorum continues to be of major economic concern for farmers. Thus, chemical control remains a prevalent disease management strategy. Pesticide evaluations were conducted in Illinois, Iowa, Michigan, Minnesota, New Jersey, and Wisconsin from 2009 to 2016, for a total of 25 site-years (n = 2,057 plot-level data points). These studies were used in network meta-analyses to evaluate the impact of 10 popular pesticide active ingredients, and seven common application timings on SSR control and yield benefit, compared with not treating with a pesticide. Boscalid and picoxystrobin frequently offered the best reductions in disease severity and best yield benefit (P < 0.0001). Pesticide applications (one- or two-spray programs) made during the bloom period provided significant reductions in disease severity index (DIX) (P < 0.0001) and led to significant yield benefits (P = 0.0009). Data from these studies were also used in nonlinear regression analyses to determine the effect of DIX on soybean yield. A three-parameter logistic model was found to best describe soybean yield loss (pseudo-R2 = 0.309). In modern soybean cultivars, yield loss due to SSR does not occur until 20 to 25% DIX, and considerable yield loss (-697 kg ha-1 or -10 bu acre-1) is observed at 68% DIX. Further analyses identified several pesticides and programs that resulted in greater than 60% probability for return on investment under high disease levels.

Assessing Variation in US Soybean Seed Composition (Protein and Oil).

Soybean [Glycine max (L.) Merr.] seed composition and yield are a function of genetics (G), environment (E), and management (M) practices, but contribution of each factor to seed composition and yield are not well understood. The goal of this synthesis-analysis was to identify the main effects of G, E, and M factors on seed composition (protein and oil concentration) and yield. The entire dataset (13,574 data points) consisted of 21 studies conducted across the United States (US) between 2002 and 2017 with varying treatments and all reporting seed yield and composition. Environment (E), defined as site-year, was the dominant factor accounting for more than 70% of the variation for both seed composition and yield. Of the crop management factors: (i) delayed planting date decreased oil concentration by 0.007 to 0.06% per delayed week (R 2∼0.70) and a 0.01 to 0.04 Mg ha-1 decline in seed yield per week, mainly in northern latitudes (40-45 N); (ii) crop rotation (corn-soybean) resulted in an overall positive impact for both seed composition and yield (1.60 Mg ha-1 positive yield difference relative to continuous soybean); and (iii) other management practices such as no-till, seed treatment, foliar nutrient application, and fungicide showed mixed results. Fertilizer N application in lower quantities (10-50 kg N ha-1) increased both oil and protein concentration, but seed yield was improved with rates above 100 kg N ha-1. At southern latitudes (30-35 N), trends of reduction in oil and increases in protein concentrations with later maturity groups (MG, from 3 to 7) was found. Continuing coordinated research is critical to advance our understanding of G × E × M interactions.

TBA225, a fusion toxoid vaccine for protection and broad neutralization of staphylococcal superantigens.

Superantigens (SAgs) play a major role in the pathogenesis of Staphylococcus aureus and are associated with several diseases, including food poisoning, bacterial arthritis, and toxic shock syndrome. Monoclonal antibodies to these SAgs, primarily TSST-1, SEB and SEA have been shown to provide protection in animal studies and to reduce clinical severity in bacteremic patients. Here we quantify the pre-existing antibodies against SAgs in many human plasma and IVIG samples and demonstrate that in a major portion of the population these antibody titers are suboptimal and IVIG therapy only incrementally elevates the anti-SAg titers. Our in vitro neutralization studies show that a combination of antibodies against SEA, SEB,and TSST-1 can provide broad neutralization of staphylococcal SAgs. We report a single fusion protein (TBA225) consisting of the toxoid versions of TSST-1, SEB and SEA and demonstrate its immunogenicity and protective efficacy in a mouse model of toxic shock. Antibodies raised against this fusion vaccine provide broad neutralization of purified SAgs and culture supernatants of multiple clinically relevant S. aureus strains. Our data strongly supports the use of this fusion protein as a component of an anti-virulence based multivalent toxoid vaccine against S. aureus disease.

Defining Optimal Soybean Sowing Dates across the US.

Global crop demand is expected to increase by 60-110% by 2050. Climate change has already affected crop yields in some countries, and these effects are expected to continue. Identification of weather-related yield-limiting conditions and development of strategies for agricultural adaptation to climate change is essential to mitigate food security concerns. Here we used machine learning on US soybean yield data, collected from cultivar trials conducted in 27 states from 2007 to 2016, to examine crop sensitivity to varying in-season weather conditions. We identified the month-specific negative effect of drought via increased water vapor pressure deficit. Excluding Texas and Mississippi, where later sowing increased yield, sowing 12 days earlier than what was practiced during this decade across the US would have resulted in 10% greater total yield and a cumulative monetary gain of ca. US$9 billion. Our data show the substantial nation- and region-specific yield and monetary effects of adjusting sowing timing and highlight the importance of continuously quantifying and adapting to climate change. The magnitude of impact estimated in our study suggest that policy makers (e.g., federal crop insurance) and laggards (farmers that are slow to adopt) that fail to acknowledge and adapt to climate change will impact the national food security and economy of the US.

Spectral Data Collection by Dual Field-Of-View System Under Changing Atmospheric Conditions-A Case Study of Estimating Early Season Soybean Populations.

There is an increasing interest in using hyperspectral data for phenotyping and crop management while overcoming the challenge of changing atmospheric conditions. The Piccolo dual field-of-view system collects up- and downwelling radiation nearly simultaneously with one spectrometer. Such systems offer great promise for crop monitoring under highly variable atmospheric conditions. Here, the system's utility from a tractor-mounted boom was demonstrated for a case study of estimating soybean plant populations in early vegetative stages. The Piccolo system is described and its performance under changing sky conditions are assessed for two replicates of the same experiment. Plant population assessment was estimated by partial least squares regression (PLSR) resulting in stable estimations by models calibrated and validated under sunny and cloudy or cloudy and sunny conditions, respectively. We conclude that the Piccolo system is effective for data collection under variable atmospheric conditions, and we show its feasibility of operation for precision agriculture research and potential commercial applications.

Resistance against Sclerotinia sclerotiorum in soybean involves a reprogramming of the phenylpropanoid pathway and up-regulation of antifungal activity targeting ergosterol biosynthesis.

Sclerotinia sclerotiorum, a predominately necrotrophic fungal pathogen with a broad host range, causes a significant yield-limiting disease of soybean called Sclerotinia stem rot. Resistance mechanisms against this pathogen in soybean are poorly understood, thus hindering the commercial deployment of resistant varieties. We used a multiomic approach utilizing RNA-sequencing, gas chromatography-mass spectrometry-based metabolomics and chemical genomics in yeast to decipher the molecular mechanisms governing resistance to S. sclerotiorum in soybean. Transcripts and metabolites of two soybean recombinant inbred lines, one resistant and one susceptible to S. sclerotiorum were analysed in a time course experiment. The combined results show that resistance to S. sclerotiorum in soybean is associated in part with an early accumulation of JA-Ile ((+)-7-iso-jasmonoyl-L-isoleucine), a bioactive jasmonate, increased ability to scavenge reactive oxygen species, and importantly, a reprogramming of the phenylpropanoid pathway leading to increased antifungal activities. Indeed, we noted that phenylpropanoid pathway intermediates, such as 4-hydroxybenzoate, cinnamic acid, ferulic acid and caffeic acid, were highly accumulated in the resistant line. In vitro assays show that these metabolites and total stem extracts from the resistant line clearly affect S. sclerotiorum growth and development. Using chemical genomics in yeast, we further show that this antifungal activity targets ergosterol biosynthesis in the fungus, by disrupting enzymes involved in lipid and sterol biosynthesis. Overall, our results are consistent with a model where resistance to S. sclerotiorum in soybean coincides with an early recognition of the pathogen, leading to the modulation of the redox capacity of the host and the production of antifungal metabolites.

Validating Sclerotinia sclerotiorum Apothecial Models to Predict Sclerotinia Stem Rot in Soybean (Glycine max) Fields.

In soybean, Sclerotinia sclerotiorum apothecia are the sources of primary inoculum (ascospores) critical for Sclerotinia stem rot (SSR) development. We recently developed logistic regression models to predict the presence of apothecia in irrigated and nonirrigated soybean fields. In 2017, small-plot trials were established to validate two weather-based models (one for irrigated fields and one for nonirrigated fields) to predict SSR development. Additionally, apothecial scouting and disease monitoring were conducted in 60 commercial fields in three states between 2016 and 2017 to evaluate model accuracy across the growing region. Site-specific air temperature, relative humidity, and wind speed data were obtained through the Integrated Pest Information Platform for Extension and Education (iPiPE) and Dark Sky weather networks. Across all locations, iPiPE-driven model predictions during the soybean flowering period (R1 to R4 growth stages) explained end-of-season disease observations with an accuracy of 81.8% using a probability action threshold of 35%. Dark Sky data, incorporating bias corrections for weather variables, explained end-of-season disease observations with 87.9% accuracy (in 2017 commercial locations in Wisconsin) using a 40% probability threshold. Overall, these validations indicate that these two weather-based apothecial models, using either weather data source, provide disease risk predictions that both reduce unnecessary chemical application and accurately advise applications at critical times.

Benefits and Profitability of Fluopyram-Amended Seed Treatments for Suppressing Sudden Death Syndrome and Protecting Soybean Yield: A Meta-Analysis.

A meta-analytic approach was used to summarize data on the effects of fluopyram-amended seed treatment on sudden death syndrome (SDS) and yield of soybean (Glycine max L.) in over 200 field trials conducted in 12 U.S. states and Ontario, Canada from 2013 to 2015. In those trials, two treatments-the commercial base (CB), and CB plus fluopyram (CBF)-were tested, and all disease and yield data were combined to conduct a random-effects and mixed-effects meta-analysis (test of moderators) to estimate percent control and yield response relative to CB. Overall, a 35% reduction in foliar disease and 295 kg/ha (7.6%) increase in yield were estimated for CBF relative to CB. Sowing date and geographic region affected both estimates. The variation in yield response was explained partially by disease severity (19%), geographic region (8%), and sowing date (10%) but not by the resistance level of the cultivar. The probability of not offsetting the cost of fluopyram was estimated on a range of grain prices and treatment cost combinations. There was a high probability (>80%) of yield gains when disease level was high in any cost-price combinations tested but very low when the foliar symptoms of the disease were absent.

Revisiting Planting Date and Cultivar Effects on Soybean Sudden Death Syndrome Development and Yield Loss.

The impact of today's optimal planting dates on sudden death syndrome (SDS) (caused by Fusarium virguliforme) development and soybean yield loss are not yet well understood. Field trials established in Hancock, Wisconsin during 2013 and 2014 investigated interactions between planting date and cultivar on SDS development and soybean yield. In 2013, disease index (DX) levels differed among cultivars, but results showed no difference between the 6 May and 24 May planting dates. Significantly lower DX levels were observed for the 17 June date. Greatest yields were found in the 6 May planting date, and yield losses were 720 (17%), 770 (20%), and 400 kg ha-1 (12%) for the 6 May, 24 May, 17 and June planting dates, respectively. In 2014, cultivars again differed for DX, but results showed highest DX levels in the 5 May planting date, with little disease observed in the 22 May and 11 June dates. Yield losses were 400 (12%) and 270 kg ha-1 (9%) for the 5 May and 22 May dates, respectively, but no difference was found in the 11 June date. Despite the most symptom development, these results suggest early May planting coupled with appropriate cultivar selection provides maximum yield potential and profitability in Wisconsin.

Effect of Maize Hybrid and Foliar Fungicides on Yield Under Low Foliar Disease Severity Conditions.

Foliar fungicide use in the U.S. Corn Belt increased in the last decade; however, questions persist pertaining to its value and sustainability. Multistate field trials were established from 2010 to 2012 in Illinois, Iowa, Ohio, and Wisconsin to examine how hybrid and foliar fungicide influenced disease intensity and yield. The experimental design was in a split-split plot with main plots consisting of hybrids varying in resistance to gray leaf spot (caused by Cercospora zeae-maydis) and northern corn leaf blight (caused by Setosphaera turcica), subplots corresponding to four application timings of the fungicide pyraclostrobin, and sub-subplots represented by inoculations with either C. zeae-maydis, S. turcica, or both at two vegetative growth stages. Fungicide application (VT/R1) significantly reduced total disease severity relative to the control in five of eight site-years (P<0.05). Disease was reduced by approximately 30% at Wisconsin in 2011, 20% at Illinois in 2010, 29% at Iowa in 2010, and 32 and 30% at Ohio in 2010 and 2012, respectively. These disease severities ranged from 0.2 to 0.3% in Wisconsin in 2011 to 16.7 to 22.1% in Illinois in 2010. The untreated control had significantly lower yield (P<0.05) than the fungicide-treated in three site-years. Fungicide application increased the yield by approximately 6% at Ohio in 2010, 5% at Wisconsin in 2010 and 6% in 2011. Yield differences ranged from 8,403 to 8,890 kg/ha in Wisconsin 2011 to 11,362 to 11,919 kg/ha in Wisconsin 2010. Results suggest susceptibility to disease and prevailing environment are important drivers of observed differences. Yield increases as a result of the physiological benefits of plant health benefits under low disease were not consistent.

Climate-induced reduction in US-wide soybean yields underpinned by region- and in-season-specific responses.

The United States is one of the largest soybean exporters in the world. Production is concentrated in the upper Midwest(1). Much of this region is not irrigated, rendering soybean production systems in the area highly sensitive to in-season variations in weather. Although the influence of in-season weather trends on the yields of crops such as soybean, wheat and maize has been explored in several countries(2-6), the potentially confounding influence of genetic improvements on yields has been overlooked. Here we assess the effect of in-season weather trends on soybean yields in the United States between 1994 and 2013, using field trial data, meteorological data and information on crop management practices, including the adoption of new cultivars. We show that in-season temperature trends had a greater impact on soybean yields than in-season precipitation trends over the measurement period. Averaging across the United States, we show that soybean yields fell by around 2.4% for every 1 °C rise in growing season temperature. However, the response varied significantly among individual states, ranging from -22% to +9%, and also with the month of the year in which the warming occurred. We estimate that year-to-year changes in precipitation and temperature combined suppressed the US average yield gain by around 30% over the measurement period, leading to a loss of US$11 billion. Our data highlight the importance of developing location-specific adaptation strategies for climate change based on early-, mid- and late-growing season climate trends.