Illinois Soybean Research Articles

Optimum source, rate, timing, and placement of phosphorus fertilizer for Illinois soybean

AbstractConsistent increases in soybean (Glycine max L.) grain yields over the past decades in Illinois have co‐increased P demand, but—as for many US Midwest states—P fertilizer recommendations are outdated. We evaluated soybean grain yield, P uptake and removal with grain harvest, and P use efficiency in 4 site‐years of field trials on Argiudolls‐Endoaquolls and Fragiudalfs‐Hapludalfs under annual P application treatments of source (monoammonium phosphate [MAP], diammonium phosphate [DAP], triple superphosphate [TSP]), rate (partial vs. full maintenance rate), and timing–placement combination (fall and spring broadcast, spring banding). Substituting ammonium phosphate fertilizers, the most commonly used P fertilizers in the US Midwest, with N‐free TSP supported similar yields and resulted in similar P removal as hypothesized, while avoiding co‐applied N that can be subject to losses primarily via leaching. Soybean yield and grain P removal were unresponsive to rate, timing, and placement even at the partial rate, although banding and spring application may have reduced N loss risk for MAP and DAP. Given the challenges in accurately estimating P removal rate by grain harvest due to the variability in yield and grain P concentrations across years, assessment of P use efficiency should focus on long‐term balance between fertilization (input) and crop removal (output).

Virulence, Aggressiveness, and Fungicide Sensitivity of Phytophthora spp. Associated with Soybean in Illinois.

Phytophthora root and stem rot (PRR), caused by Phytophthora sojae, is one of the most devastating oomycete diseases of soybean in Illinois. Single resistant genes (Rps) are used to manage this pathogen but P. sojae has adapted to Rps, causing failure of resistance in many regions. In addition to P. sojae, recent reports indicate that P. sansomeana could also cause root rot in soybean. Soil samples and symptomatic plants were collected across 40 Illinois counties between 2016 and 2018. P. sojae (77%) was more abundant than P. sansomeana (23%) across Illinois fields. Both species were characterized by virulence, aggressiveness, and fungicide sensitivity. Virulence of all P. sojae isolates was evaluated using the hypocotyl inoculation technique in 13 soybean differentials. Aggressiveness was evaluated in the greenhouse by inoculating a susceptible cultivar and measuring root and shoot dry weight. On average, P. sojae isolates were able to cause disease on six soybean differentials. P. sojae was more aggressive than P. sansomeana. All isolates were sensitive to azoxystrobin, ethaboxam, mefenoxam, and metalaxyl. The characterization of the population of species associated with PRR will inform management decisions for this disease in Illinois.

A Survey of Soybean Cyst Nematode Population Densities and Phenotypes in Illinois: 2018 and 2020

In Illinois, soybean cyst nematode (SCN; Heterodera glycines) has been estimated to cause over $15 million in losses as recently as 2021. In addition, populations of SCN insensitive to PI 88788-derived resistance, the most commonly used SCN resistance source in commercially available soybeans, continues to increase in soybean production areas in the United States. Despite this, awareness of this pathogen and its current status in the state is relatively lacking. A survey was conducted in 2018 and 2020 to assess the presence, population level, and virulence phenotypes in Illinois soybean fields. Soil samples were collected and processed for SCN eggs after harvest each year, and samples with sufficient numbers of SCN eggs were virulence tested using the Hg type test. A total of 95 samples were collected, representing 46 counties. Eighty-seven percent of fields sampled contained SCN eggs, with 38% containing population densities over 2,000 eggs/100 cm3. All samples included in the Hg type test had a female index > 10 on indicator line PI 88788, indicating insensitivity to this resistance source. Extrapolated across soybean production acres, these data indicate that approximately 4.4 million acres of soybeans contain levels of SCN requiring SCN management, and over 54% of acres using cultivars derived from PI 88788 are not fully protected from this pathogen.

Optimal county-level crop yield prediction using MODIS-based variables and weather data: A comparative study on machine learning models

Accurate crop yield prediction for more precise forecasting of price volatility in crop markets, better agricultural planning and enhanced national food security is one of the important utilities of crop monitoring systems. Due to the spatiotemporal nonlinear characteristic of crop yields, recent studies have actively used various machine learning approaches to predict crop yields. However, there are few studies that have compared these approaches, and even in those cases, the comparison was restricted to one or two crops or a specific region. In this study, we evaluated seven popular machine learning approaches, for the same input variables, over three crops: paddy rice in South Korea and corn and soybean in Illinois and Iowa, USA. Based on the data from April to September, six time-series scenarios of different range of months for each crop were tested for prediction accuracy with 14-year (2003-2016). The time-series data include vegetation indices from Moderate resolution imaging spectroradiometer (MODIS), weather data, crop yield statistics, and a land cover map of county-level spatial resolution and 16-day-aggregated temporal resolution. In the results, regardless of crop type, support vector machine (SVM) presented the most accurate result with the lowest average root mean square error (RRMSE), in comparison with a decision tree (DT), random forest (RF), artificial neural network (ANN), stacked-sparse autoencoder (SSAE), convolutional neural network (CNN), and long short-term memory (LSTM). Also, we derived best cases for each crop yield prediction out of six time-series scenarios, which were May – September data for rice and corn and June – August data for soybean for the test sites. Considering all of the findings together, SVM is an appropriate method for crop yield prediction based on MODIS-based vegetation indices and weather data of county-level spatial resolution and approximately one-half-month temporal resolution.

First report of Mycoleptodiscus terrestris causing root rot of soybean in Indiana.

During the summers in 2019 and 2020, 137 soybean (Glycine max (L.) Merr) seedlings (V1-V3 stage) showing stunting, delayed emergence, and/or crown lesions were collected at Purdue's Agronomy Center for Research and Education in West Lafayette, Indiana. Four seedlings were stunted with reddish-brown girdled lesions along the hypocotyl and crown, rotted tap and lateral roots, and brown discoloration of the cortex and vascular tissues. Four fungal isolates (AC4, AC58, AC96, and AC127) were recovered by plating surface-sterilized symptomatic root tissue onto water agar plates and incubating on the benchtop until mycelia emerged. The growing hyphal tips were transferred to the semi-selective medium DCPA (Andrews and Pitt 1986). On potato dextrose agar, the fungal colonies developed olivaceous green mycelia which melanized into a mat of black microsclerotia with time and no conidia were observed. On 1.5% water agar plates amended with twice autoclaved soybean leaf and root tissue collected from flowering soybean plants, conidia were formed in sporodochia in darkness at 28 οC within one week. Conidia were 1-2 septate, cylindrical with two setae on either end, and measured 20.8 to 26.4 x 4 to 5.6 μm (average 23.9 x 4.7 μm, n=20). The morphological characters matched with the description of Mycoleptodiscus terrestris (Gerd.) Ostaz (Gerdemann 1953). Species identification was further confirmed by sequencing the internal transcribed spacers (ITS) region of rDNA amplified by ITS1 and ITS4 primers (White et al. 1990) and the translation elongation factor 1 alpha (TEF1-α) gene using 983F and 1567R primers with annealing temperature at 53 ○C (Rehner and Buckley 2005). The sequences were deposited in GenBank under the following accession numbers: ITS: MW002684, MT998441, MW010258, and MW010260; and TEF1-α: MW015941-MW015944. The GenBank BLAST searches revealed 100% identity in the ITS region (accession NR_145373.1) and 99.75% identity in the TEF1-α region (MK495977.1) to M. terrestris. Pathogenicity test was conducted on soybean seedlings (cv. Williams) at V1 growth stage using a root dip assay. Isolate AC58 was grown in a modified cotton seed meal broth (CSMB) to produce microsclerotia as inoculum (Gray 1978; Shearer and Jackson 2006). Microsclerotia concentration was measured using a hemocytometer and adjusted to 1.5 x 104 per ml. Five soybean seedlings each were dipped into inoculum or sterile CSMB for 30 minutes then planted individually in vermiculite-filled Styrofoam cups placed on flooded trays in 16-hr photoperiod light racks at room temperature. Seven days after inoculation, all inoculated plants were visibly stunted with root and crown symptoms identical to field symptoms while all controls were healthy. M. terrestris was successfully re-isolated from inoculated plants, but not from the controls, and identified by morphology and sequencing as above. M. terrestris has been previously reported causing root rot of soybean in Illinois (Gray 1978) and Wisconsin (Smith et al. 1998). To our knowledge, this is the first report of M. terrestris infecting soybean in Indiana. Increased geographic distribution of this pathogen warrants more attention for its control. M. terrestris has been proposed as a biological control agent against multiple aquatic weeds (Verma and Charudattan 1993; Shearer and Jackson 2006). Introduction of this fungus into soybean production regions should be avoided.

Comparison of Phytophthora sojae Populations in Iowa and Nebraska to Identify Effective Rps Genes for Phytophthora Stem and Root Rot Management

Phytophthora stem and root rot (PSRR) of soybean, caused by the oomycete Phytophthora sojae, is prevalent in Iowa and Nebraska. Reducing losses to PSRR primarily relies on growing cultivars with specific resistance (Rps) genes. Predominant genes used in commercial soybean cultivars include Rps 1a, Rps 1c, Rps 1k, and Rps 3a. Knowing which Rps gene to deploy depends on knowledge of which genes are effective against the pathogen. From 2016 to 2018, 326 isolates of P. sojae from were recovered from fields in Iowa and Nebraska and classified into pathotypes based on their virulence on 15 soybean genotypes. A total of 15 and 10 pathotypes were identified in Iowa and Nebraska, respectively. Almost all isolates were virulent on Rps 1a, and over 70% of isolates were virulent on Rps 1c and Rps 1k. Only 2.3% of isolates from Iowa were virulent on Rps 3a. Among commercial soybean cultivars tested in the Illinois Soybean Variety trials from 2010 to 2020, Rps 1c was always the most frequently reported gene, followed by Rps 1k. In contrast, Rps 1a and Rps 3a were present in less than 10% and less than 5% of the cultivars tested, respectively. Because many of the P. sojae isolates in our study were virulent on Rps 1a, Rps 1c, and Rps 1k, soybean cultivars with these genes are unlikely to provide protection against PSRR unless they have a high level of partial resistance.

First Report of Rhizoctonia solani AG 11 Causing Stem Rot of Phaseolus vulgaris in Idaho

In July 2018, bean plants (cv. Viva pink) at one location in south central Idaho were observed to have reduced growth with approximately 20% of the plants affected over 1 ha. Visual assessment showed that the below-ground parts of stems consistently displayed brown lesions up to 2 cm in length, which sometimes girdled the entire plant. To determine the causal agent, affected material (approximately 5 mm³) was surface disinfested in sodium hypochlorite (2%) for 30 s, rinsed twice in sterile water, and plated onto water agar medium amended with penicillin G (0.2 g/liter) and streptomycin sulfate (0.8 g/liter). After 3 days, colonies of Rhizoctonia solani were identified based on the presence of relatively broad (8 to 12 μm), septate hyphae with right-angle branching. Hyphal tips of R. solani colonies were transferred to potato dextrose agar (PDA) and grown for 2 weeks at 21°C. DNA was then extracted from hyphae taken from a single representative isolate (DB23). Anastomosis group (AG) was identified by ribosomal DNA ITS sequencing of isolate DB23. DNA was extracted and sequenced as previously described (Woodhall et al. 2013). The resulting sequence (GenBank no. MT256181) was over 99% identical with isolates previously identified as R. solani AG 11 (LC215403). To confirm pathogenicity, 2-week-old pinto bean plants (cv. Windbreaker) were each inoculated by placing a fully colonized 10-mm² plug taken from a 2-week-old PDA culture of isolate DB23 next to the stem 10 mm below the soil surface. Sterile PDA plugs were placed next to the stems of control plants. Each treatment was replicated five times. Plants were grown in a greenhouse at 21°C in potting compost (Scotts, Marysville, OH) under light for 16 h a day. After 5 weeks, each plant was assessed for disease using a previously described stem disease severity scale (Carling and Leiner 1990). No symptoms were present in the control plants. Plants inoculated with isolate DB23 had an average stem disease severity score of 2.6 (from a maximum of 4), and R. solani was consistently reisolated, thereby confirming Koch’s postulates. To our knowledge, this is the first report of R. solani AG 11 in Idaho on any host. Previously, R. solani AG 11 has been reported on Phaseolus vulgaris in Turkey (Eken and Demirci 2004) and on lily in Japan (Misawa et al. 2017). AG 11 has also been associated with soybeans in Arkansas (Ajayi-Oyetunde and Bradley 2017; Carling et al. 1994) and Illinois (Ajayi-Oyetunde and Bradley 2017). AG 11 has also been reported on rice in Arkansas (Carling et al. 1994; Spurlock et al. 2016) and sweet corn in New York (Ohkura et al. 2009). In western Australia, it was found on wheat and lupin, and these isolates were pathogenic to potatoes in controlled environment studies (Carling et al. 1994). Idaho is a major producer of wheat, potatoes, and dry beans; the presence of soilborne R. solani AG 11 also needs to be considered when diagnosing and managing Rhizoctonia diseases of these crops.

Registration of ‘S14‐9017GT’ soybean cultivar with high yield, resistance to multiple diseases, and high seed oil content

Abstract‘S14‐9017GT’ (Reg. no. CV‐536, PI 693755) is a Maturity Group V high‐yielding glyphosate‐tolerant soybean [Glycine max (L) Merr.] cultivar developed and released in 2017 by the University of Missouri–Fisher Delta Research Center Soybean Breeding Program. S14‐9017GT was developed by conventional breeding from a cross between ‘LD07‐3419’ and ‘S08‐9727RR1’. The parent S08‐9727RR1 is a breeding line that carries the GTS 40‐3‐2 transgene discovered by Monsanto Co. The other parent, LD07‐3419, is a breeding line developed by the University of Illinois soybean breeding program. Before release, S14‐9017GT was tested in 122 environments in Missouri and other southern states from 2015 to 2018 for yield and other agronomic traits. On average, S14‐9017GT yielded 4,314 kg ha−1, which is higher than the mean of all the tests, which was 4,124 kg ha−1. S14‐9017GT seed contains 378 g kg−1 protein and 246 g kg−1 oil on a dry seed weight basis. It exhibits a relative maturity of 5.3, determinate growth habit, white flowers, light tawny pubescence, and tan pod wall. It has yellow seed with intermediate luster and black hila and a seed weight of 14.9 g per 100 seed. Plants of S14‐9017GT are 81 cm tall with resistance to lodging. It is resistant to stem canker, frogeye leaf spot, cyst nematode (races 1, 2, 3, and 5), reniform nematode, peanut root‐knot nematode, and sudden death syndrome and has field tolerance to phytophthora root rot. In addition to its high yield potential, high seed oil content, and broad adaptation across southern states, S14‐9017GT is also a suitable alternative for farmers who want to grow and save seed of glyphosate‐tolerant soybean cultivars.

Remote sensing phenological monitoring framework to characterize corn and soybean physiological growing stages

The phenological dynamics of crops reflect the response and feedback of agricultural systems to climate and environmental constraints, and have significant controls on carbon and nutrient cycling across the globe. Remote monitoring of crop phenological dynamics in a consistent and systematic manner is vitally crucial for optimizing the farm management activities and evaluating the agricultural resilience to extreme weather conditions and future climate change. Yet our ability to retrieve crop growing stages with satellite time series is limited. The remotely sensed phenological transition dates may not be characteristic of crop physiological growing stages. The objective of this study is to develop a remote sensing phenological monitoring framework that can reconcile satellite-based phenological measures with ground-based crop growing observations, with corn and soybean in Illinois as a case study. The framework comprises three key components: time series phenological pre-processing, time series phenological modeling, and time series phenological characterization. As an exploratory prototype, the framework retrieved a total of 56 phenological transition dates that were subsequently evaluated with the district-level ground phenological observations. The results indicated that the devised framework can adequately retrieve a wide range of physiological growing stages for corn and soybean in Illinois, with R square greater than 0.6 and RMSE less than 1 week for most stages. The devised framework largely extends the limited satellite phenological measures to a range of phenological transition dates that are characteristic of essential crop growing stages. It paves the way for formulating standard crop phenological monitoring protocols via remote sensing. The wealth of retrieved phenological characteristics open up unique opportunities to enhance our understanding of the complex mechanisms underlying the crop growth in response to varying environmental stresses, and to make more adaptive farm management strategies towards sustained agricultural development.

Characterization of Septoria brown spot disease development and yield effects on soybean in Illinois

Brown spot, caused by Septoria glycines Hemmi, is a highly prevalent foliar disease of soybean (Glycine max (L.) Merr.). Despite its wide distribution, the development of Septoria brown spot and its relationship with yield reduction remain poorly characterized. In this study, we conducted replicated multi-location inoculated field trials to characterize the disease development and evaluated the relationship between Septoria brown spot and soybean yield. Multiple components of disease and yield were rated weekly to characterize disease development. Fungicide treatments had a significant effect on ratings of vertical progress of the disease and chlorotic area at the end of the season. There were also significant differences between the fungicide treatments for the Area Under the Disease Progress Curve (AUDPC) of all the disease components, including necrotic area and defoliation rate. Soybean yield was negatively correlated with the vertical progress of the disease (r = −0.36). The vertical progress was the best linear predictor of yield with an R2 = 0.08 for the end of the season rating and an R2 = 0.2 for the AUDPC. A variance component analysis of the data showed that location was the most critical factor, illustrating the large effect of local environmental conditions on the disease. There was no statistically significant effect of the fungicide treatments on yield. Power analyses indicated that at least eight locations are needed to detect an effect of 269 kg ha−1. Our results provide useful information in the characterization of the disease development and for estimations of yield damage.

Genetic Structure of Rhizoctonia solani AG-2-2IIIB from Soybean in Illinois, Ohio, and Ontario

Rhizoctonia solani AG-2-2IIIB is an important seedling pathogen of soybean in North America and other soybean-growing regions around the world. There is no information regarding the population genetics of field populations of R. solani associated with soybean seedling disease. More specifically, information regarding genetic diversity, the mode of reproduction, and the evolutionary factors that shape different R. solani populations separated in time and space are lacking. We exploited genotyping by sequencing as a tool to assess the genetic structure of R. solani AG-2-2IIIB populations from Illinois, Ohio, and Ontario and investigate the reproductive mode of this subgroup. Our results revealed differences in genotypic diversity among three populations, with the Ontario population having greatest diversity. An overrepresentation of multilocus genotypes (MLGs) and a rejection of the null hypothesis of random mating in all three populations suggested clonality within each population. However, phylogenetic analysis revealed long terminal multifurcating branches for most members of the Ontario population, suggesting a mixed reproductive mode for this population. Analysis of molecular variance revealed low levels of population differentiation, and sharing of similar MLGs among populations highlights the role of genotype flow as an evolutionary force shaping population structure of this subgroup.

First Report of Red Crown Rot of Soybeans Caused by Calonectria ilicicola (Anamorph: Cylindrocladium parasiticum) in Illinois

HomePlant DiseaseVol. 103, No. 7First Report of Red Crown Rot of Soybeans Caused by Calonectria ilicicola (Anamorph: Cylindrocladium parasiticum) in Illinois PreviousNext DISEASE NOTESFirst Report of Red Crown Rot of Soybeans Caused by Calonectria ilicicola (Anamorph: Cylindrocladium parasiticum) in IllinoisN. Kleczewski, D. Plewa, C. Kangas, E. Phillippi, and V. KleczewskiN. Kleczewski†Corresponding author: N. Kleczewski; E-mail Address: nathank@illinois.eduhttp://orcid.org/0000-0001-5671-6727University of Illinois, Department of Crop Sciences, Urbana, IL 61801Search for more papers by this author, D. PlewaUniversity of Illinois, Department of Crop Sciences, Urbana, IL 61801Search for more papers by this author, C. KangasUniversity of Illinois, Department of Crop Sciences, Urbana, IL 61801Search for more papers by this author, E. PhillippiUniversity of Illinois, Department of Crop Sciences, Urbana, IL 61801Search for more papers by this author, and V. KleczewskiGrowmark Inc., Bloomington, IL 61701Search for more papers by this authorAffiliationsAuthors and Affiliations N. Kleczewski1 † D. Plewa1 C. Kangas1 E. Phillippi1 V. Kleczewski2 1University of Illinois, Department of Crop Sciences, Urbana, IL 61801 2Growmark Inc., Bloomington, IL 61701 Published Online:7 May 2019https://doi.org/10.1094/PDIS-01-19-0105-PDNAboutSectionsSupplemental ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InRedditEmailWechat In September 2018 soybean (Glycine max) disease was observed in a commercial field located near Pittsfield, Illinois. Signs and symptoms resembled those of red crown rot, caused by Calonectria ilicicola (Hartman et al. 2015). Red to brown lesions were observed at the stem base, and red, raised perithecia were observed on the lesion surface. The root systems of affected plants were black and severely rotted, often with only taproots remaining after removal from the soil. The inner pith of the lower stem was light gray in appearance. Foliar symptoms included interveinal chlorosis and defoliation. Disease incidence was estimated at 75%, and yield loss was estimated at approximately 25%. Affected plants were harvested, placed in a cooler on ice, and transported to the University of Illinois. Stems were surface sterilized in 10% bleach (30 s), 75% ethanol (30 s), followed by two rinses in sterile distilled water bleach, and allowed to air dry under a biosecurity cabinet. Isolations were made from sections of symptomatic stems on Petri plates containing half-strength potato dextrose agar placed under a light bank equipped with cool fluorescent lights set on a 12-h light/dark cycle at 20°C. Fungi were isolated from 100% of sections plated, and individual isolates were collected via hyphal tip technique. Isolated colonies initially produced white, aerial mycelia that later turned burnt red to brown with age. Conidia were hyaline, cylindrical with zero to three septae, and measured 28.5 to 87.1 × 3.9 to 7.6 μm. Perithecia were reddish orange, oval to globose, 202.6 to 424.7 μm tall, and 100.3 to 353.8 μm in width. Asci were hyaline and clavate and measured 111.0 to 206.1 × 10.5 to 28.6 μm, and each contained eight falcate ascospores with one to two septa. Morphological characteristics identified this organism as C. ilicicola (anamorph: Cylindrocladium parasiticum [Hartman et al. 2015]). A 600-bp fragment of the β-tubulin gene was amplified using the T1 (O’Donnell and Cigelnik 1997) and Bt2b (Glass and Donaldson 1995) primers. Thermocycler conditions were as follows: initial denaturation for 10 min at 96°C followed by 30 cycles of 96°C for 15 s, 55°C for 35 s, and 75°C for 35 s, with a final elongation step of 75°C for 2 min (Crous et al. 1999). Polymerase chain reaction products were purified and sequenced at the Roy J. Carver Biotechnology Center at the University of Illinois at Urbana-Champaign. A sequence of the representative isolate 3A was submitted to GenBank (MK370992). Comparisons with GenBank showed 99% similarity with multiple sequences of C. ilicicola (top match JX0693831.1, bit score 1,107, E = 0.0, 607/610 bp). Pathogenicity was confirmed by inoculating 3-week-old seedlings of soybean cultivar Pioneer 93Y25 grown in in 15-cm-diameter pots containing autoclaved greenhouse potting media. Six plants were present in each pot, and three pots were used per treatment. Inoculation was conducted by drenching with a 105 conidia/ml suspension acquired from 14-day-old, single-spore fungal isolates. Three unamended pots were used as controls. Plants were watered daily and maintained in a greenhouse set to 23°C and a 14-h/10-h light/dark cycle. After 14 days, inoculated plants showed similar basal stem symptoms and after 30 days developed interveinal chlorosis on leaves. C. ilicicola was reisolated from diseased plants and confirmed through microscopy. This pathogen may pose a threat to >440,000,000 ha of soybean in Illinois, not including surrounding states. Our understanding of impact and management under Midwest production practices is currently unknown.The author(s) declare no conflict of interest.

Many plant pathogenic Pseudomonas savastanoi pv glycinea isolates possess an inactive quorum sensing ahlR gene via a point mutation.

Many plant bacterial pathogens monitor their group behaviour and their population density via production of N-acyl homoserine lactone signals which regulate the expression of several genes via the LuxI/R homologs. This regulatory network, termed quorum sensing (QS), is present in the soybean bacterial pathogen Pseudomonas savastanoi pv glycinea (Psg). The sequenced genomes of two strains of Psg, race 4 and B076, contain an N-acyl homoserine lactone (AHL) based LuxI/R QS system named AhlI/R. While studying the QS system of Psg strains race 4 and B076 isolated in USA, LMG5066 in New Zealand and IBSBF355 in Brazil, we found that B076, LMG5066 and IBSBF355 possess a point mutation in the ahlR gene that causes a frameshift resulting in a truncated AhlR protein. Psg race 4 does not possess the mutation in ahlR and the QS system is functional. The same mutation in the ahlR gene was found to be also present in 9 of 19 Psg strains isolated from diseased soybean in Illinois. Phenotypic analysis of strains showed that swarming motility is repressed whereas phosphate solubilisation was activated by QS in Psg. Analysing the secretome, we also found that four proteins were under QS regulation.

Innovative pheno-network model in estimating crop phenological stages with satellite time series

Large-scale remote monitoring of crop phenological development is vital for scheduling farm management activities and estimating crop yields. Tracking crop phenological progress is also crucial to understand agricultural responses to environmental stress and climate change. During the past decade, time series of remotely sensed imagery has been increasingly employed to monitor the seasonal growing dynamics of crops. A variety of curve-fitting based phenological methods have been developed to estimate critical phenological transition dates. However, those phenological methods are typically parametric by making mathematical assumptions of crop phenological processes and usually require year-long satellite observations for parameter training. The assumption and constraint make those methods inadequate for phenological monitoring in heavy cloud-contaminated regions or in complex agricultural systems. The objective of this study is to estimate crop phenological stages with satellite time series using a complex network-based phenological model (i.e., “pheno-network”). The innovative pheno-network model is non-parametric without mathematically defined phenological assumptions and can be constructed with partial-year remote sensing data. Rooted in network theory, the pheno-network model characterizes the complex phenological process with spectrally defined nodes and edges. It provides an innovative network representation to model the temporal dynamics of spectral reflectance of crops throughout the growing season. With corn and soybean in Illinois as a case study, the pheno-network model was devised to estimate their phenological transition dates along the leaf senescence trajectory from 2002 to 2017. Results indicated that the estimated transition dates of corn had strong correlation with its ground-observed mature stage. As for soybean, the estimated transition dates were closely associated with its dropping leaves stage. The pheno-network model shows marked potential to advance phenological monitoring in complex agricultural diversified and intensified systems.

Improving remotely-sensed crop monitoring by NDVI-based crop phenology estimators for corn and soybeans in Iowa and Illinois, USA

Weather-related risks in crop production are not only crucial for farmers but also for market participants and policymakers since securing food supply is an important issue for society. Although crop growth condition and phenology represent essential information regarding such risks, extensive observations of these variables are virtually non-existent in many parts of the world. In this study, we developed an integrative approach to remotely monitor crop growth at a large scale. For corn and soybeans in Iowa and Illinois in the United States (2003–2015), we monitored crop growth and crop phenology with earth observation data and compared it against the United States Department of Agriculture National Agricultural Statistics Service (NASS) crop statistics. For crop phenology, we calculated three phenology metrics (i.e., start of season, end of season, and peak of season) at the pixel level from the MODIS 16-day Normalized Difference Vegetation Index (NDVI). For growth condition, we used two distinct approaches to acquire crop growth condition indicators: a process-based crop growth modeling and a satellite-NDVI-based method. Based on their pixel-wise historical distributions, we monitored relative growth strength and scaled-up that to the state-level. The estimates were compared with the crop progress and condition data of NASS. For the state-level phenology, the avg. root-mean-square-error (RMSE) of the estimates was 8.6 days for the all three metrics after bias correction. The absolute mean errors for the three metrics were smaller than 2.6 days after bias correction. For the condition, the state-level 10-day estimates showed moderate agreements with the observations (avg. RMSE = 10.02%). Notably, the condition estimates were sensitive to the severe degradation in 2003, 2012, and 2013 for both crops. In 2010, 2011 and 2013, unusually high errors occurred at the very beginning stage of growth (DOY 140–150), which attenuated over time. As the cumulative biomass and NDVI showed little change in comparison to the period mean biomass and NDVI for the spikes, this seems to be an error associated with variations in growth timing. Overall, the model using accumulated NDVI (S5) is preferable due to its performance and methodological simplicity. The proposed approach enables us to monitor crop growth for any given period and place where long-term statistics are available. It can be used to assist crop monitoring at large scales.

Cost to Produce Corn and Soybeans in Illinois—2018

Cost to Produce Corn and Soybeans in Illinois—2018

Benchmarking a triplet of official estimates

The publication of official statistics at different levels of aggregation requires a benchmarking step. Difficulties arise when a benchmarking method needs to be applied to a triplet of related estimates, at multiple stages of aggregation. For ratios of totals, external benchmarking constraints for the triplet (numerator, denominator, ratio) are that the weighted sum of denominator/numerator/ratio estimates equals to a constant. The benchmarking weight, applied to the ratio estimates, is a function of the denominator estimates. For example, the United States Department of Agriculture’s National Agricultural Statistics Service’s county-level, end-of-season acreage, production and yield estimates need to aggregate to the corresponding agricultural statistics district-level estimates, which also need to aggregate to the corresponding prepublished state-level values. Moreover, the definition of yield, as the ratio of production to harvested acreage, needs to hold at the county level, the agricultural statistics district level and the state level. We discuss different methods of applying benchmarking constraints to a triplet (numerator, denominator, ratio), at multiple stages of aggregation, where the denominator and the ratio are modeled and the numerator is derived. County-level and agricultural statistics district-level, end-of-season acreage, production and yield estimates are constructed and compared using the different methods. Results are illustrated for 2014 corn and soybean in Indiana, Iowa and Illinois.

Multi-Year Surveys Reveal Significant Decline in Western Corn Rootworm Densities in Illinois Soybean Fields

The western corn rootworm, Diabrotica virgifera virgifera LeConte, is the primary insect pest threatening corn, Zea mays L., production in many areas of the United States and is capable of inflicting substantial yield loss. In Illinois and bordering states, managing this pest is complicated by populations exhibiting resistance to crop rotation, historically an effective strategy to prevent larval injury caused by this corn specialist. Rotation resistance is characterized by ovipositional infidelity to corn accompanied by various adaptations promoting prolonged residency in soybean, Glycine max (L.) Merr. We conducted an analysis using survey data to estimate densities of western corn rootworm adults in Illinois soybean fields and compared those estimates for two discrete time periods: (1) 1997–2003 and (2) 2011 and 2013–2015. Annual surveys during each time period were conducted from late July through late August by visiting a number of randomly selected soybean fields in counties distributed throughout Illinois. Adults were sampled using sweep nets. Mean densities were determined for each United States Department of Agriculture crop reporting district and interpolated values were estimated to produce statewide contour plots. Our findings reveal a substantial decline in adult abundance in soybean fields throughout much of Illinois. Where significant differences were observed between the two survey periods, recent densities were 88% lower on average than those observed during 1997–2003. Potential factors contributing to the declines we observed and implications for managing this adaptive pest of corn are discussed.

Cost to Produce Corn and Soybeans in Illinois -- 2016

Cost to Produce Corn and Soybeans in Illinois -- 2016

Estimating the cost of pre‐harvest forward contracting corn and soybeans in Illinois before and after 2007

AbstractForward contracting historically has been the principal risk management tool of crop farmers in the United States. We use pre‐harvest forward contract bids from 1977 to 2013 in Illinois to estimate the cost of forward contracting corn and soybeans. Prior to 2007, it cost 1.55% of the October spot price or 3.74 cents/bushel on average to forward contract corn at the end of February. In 2007–2013, a period of high price volatility, the average cost increased to 3.31% or 16.40 cents/bushel. In the soybean market, before 2007 the average cost was 0.77% or 6.26 cents/bushel and thereafter increased to 1.46% or 15.62 cents/bushel. Default risk does not appear to be a determinant of forward contracting cost. However, evidence suggests that the higher cash flow risk associated with volatile market prices is likely to be the main driving cause of increasing forward contracting costs observed after 2007. [EconLit citations: Q11, Q13].