US Soybean Research Articles

Cover crops and P‐fertilizer management affect microbial activity in a US Midwest corn and soybean rotation

AbstractMicroorganisms can have a substantial effect on labile phosphorus (P), which may be lost from the soil surface and impact water quality. Changes in nutrient availability and soil health from agricultural management can affect microbial biomass carbon (MB‐C), microbial biomass phosphorus (MB‐P), and the expression of P cycling enzymes. The objective of this research was to investigate biological mechanisms affecting P availability and potential loss to runoff in a no‐till, corn (Zea mays)–soybean (Glycine max) cropping system with two cover crops (cover crop [CC] and no cover crop [NC]) and three P management treatments (fall broadcast [FB], spring injected ammonium polyphosphate [SI], and no phosphorus application [NP]). Treatments were applied to a randomized block design with three replicates. Soil samples were analyzed for MB‐P, MB‐C, phosphatase activity, and Mehlich‐III P (PM). The P supply to the soil solution was measured using diffusive gradient thin film P (PDGT). In Spring 2018, Fall 2018, and Spring 2019, all phosphatase activity was greater in CC versus NC (p < 0.01). Microbial biomass C was greater in CC compared with NC in spring but not fall samplings. On average, MB‐P was fivefold greater in the P fertilized than unfertilized treatments (p < 0.001). CCs did not change MB‐P, PM, or PDGT within FB or NP, but did affect SI fertilizer treatments. Our results suggest CC can increase potential for organic P mineralization, application of P fertilizer increases MB‐P, and an interaction between SI P fertilizer management and CC may increase P supply to the soil solution.

Extreme Risk Spillovers From US Soybean Futures Market to China's Soybean‐Linked Futures Markets

ABSTRACTThis study investigates the cross‐border risk spillovers between the US soybean futures market and Chinese soybean‐related futures markets. We first confirm the existence of strong tail dependence between US soybean futures and four Chinese soybean‐related futures by conducting a novel quantile‐Granger causality test. Second, tests under MVMQ‐CAViaR further provide evidence of risk spillovers from CBOT soybean futures to the DCE No.1 soybean, No.2 soybean, soybean meal, and soybean oil futures in value‐at‐risk at different quantiles. Lastly, results from the quantile impulse‐response function reveal the time‐varying and asymmetric property of risk spillover effects. In addition, we compare the results from two subsample periods and identify different risk spillover effects across markets at different quantiles that may contribute to the investors' decision‐making under extreme market conditions.

Leadership shift in the global soybean market: Dynamic connectedness approach (TVP-VAR)

The price transmission in international soybean market has been extensively examined. However, recent econometric advancements have enabled the application of dynamic connectedness methodology as outlined by Antonakakis and Gabauer (2017) [1], which is based on a Time-varying Parameter Vector Autoregressive (TVP-VAR) model. This approach captures the time-varying connectedness of time series, considering potential risk shock emitters and receivers. The connectedness index (Diebold and Yilmaz, Jan. 2012) [2] was developed using Generalized Forecast Error Variance Decomposition (GFEVD) and the Generalized Impulse Response Function (GIRF) (Koop et al., Sep. 1996; Pesaran and Shin, Jan. 1998) [3,4]. This study aims to understand the dynamic connectedness and price leadership. The research examined markets including the US Soybean futures market (Chicago Board of Trade), Rotterdam Port spot market representing the European soybean market, Paranaguá port representing Brazil, Argentina represented by Rosario Futures and Spot, and the Chinese domestic spot market and the Dalian futures on behalf of China. The research spanned approximately ten years, from September 2009 to May 2019. The findings suggest that the soybean market has reached a high level of maturity, able to withstand exogenous shocks for at least the past seven years. The net pairwise directional connectedness revealed dynamic and bidirectional causality. The dynamic connectedness index showed a highly connected and developed market in the West (Chicago Futures, Rotterdam, Paranaguá, and Rosario Futures and Spot). However, the connectedness between Western and Eastern markets was relatively low, indicating some level of market isolation. Furthermore, the pairwise connectedness index between Eastern market (China spot and Dalian Futures) was also considerable low. Lastly, Paranaguá and Rosario overtook Rotterdam as price-leading markets and were identified as the primary net transmitters of shocks, indirectly implying causality. Chicago, Rosario, and Paranaguá formed a triumvirate leading international prices, while Rotterdam adopted a secondary leading position, deviating from its historical role as the price leader.

Registration of high‐yielding, high‐protein soybean germplasm USDA‐N7007 derived from wild soybean PI 366122

AbstractUSDA‐N7007 is a non‐GM, maturity group (MG) VII soybean [Glycine max (L.) Merr.] (Reg. no. GP‐529, PI 705147) germplasm released by the USDA Agricultural Research Service in conjunction with the North Carolina Agricultural Research Service in December of 2023. USDA‐N7007 is a high‐yielding, high‐protein germplasm derived from wild soybean (Glycine soja Siebold & Zucc; PI 366122) and small‐seeded MG VII USDA cultivar N7103. Over 47 combined testing environments of the USDA Southern Uniform and USB Protein Diversity Tests (2018–2021), USDA‐N7007 yielded 98% of the check mean and 102% of the test mean. The average protein content of USDA‐N7007 was significantly higher (432 g kg−1) than the average check means of 402 g kg−1 and 413 g kg−1 in the USDA Southern Uniform and USB Protein Diversity Tests, respectively. Across both tests (2019–2021), the release was significantly (p < 0.05) higher in protein (+7 g kg−1), with 9% higher seed yield (+242 kg ha−1) than the recurrent parent N7103. USDA‐N7007 is resistant to lodging, southern root‐knot nematode, and stem canker. To the best of our knowledge, this is the first US release clearly demonstrating that the wild soybean genome can be incorporated into an elite cultivar to increase seed protein without a negative effect on seed yield. This release is a truly novel and valuable resource for development of future US soybean cultivars because it will be useful to improve both genetic diversity and seed protein simultaneously without a negative effect on seed yield.

Non-linear relationships between daily temperature extremes and US agricultural yields uncovered by global gridded meteorological datasets

Global agricultural commodity markets are highly integrated among major producers. Prices are driven by aggregate supply rather than what happens in individual countries in isolation. Estimating the effects of weather-induced shocks on production, trade patterns and prices hence requires a globally representative weather data set. Recently, two data sets that provide daily or hourly records, GMFD and ERA5-Land, became available. Starting with the US, a data rich region, we formally test whether these global data sets are as good as more fine-scaled country-specific data in explaining yields and whether they estimate similar response functions. While GMFD and ERA5-Land have lower predictive skill for US corn and soybeans yields than the fine-scaled PRISM data, they still correctly uncover the underlying non-linear temperature relationship. All specifications using daily temperature extremes under any of the weather data sets outperform models that use a quadratic in average temperature. Correctly capturing the effect of daily extremes has a larger effect than the choice of weather data. In a second step, focusing on Sub Saharan Africa, a data sparse region, we confirm that GMFD and ERA5-Land have superior predictive power to CRU, a global weather data set previously employed for modeling climate effects in the region.

The impact of US-Sino trade war on commodity-currency nexus

This study attempts to assess the impact of US-China bilateral trade tension on commodity-currency nexus. Eleven exported commodities and two foreign currencies are selected for this purpose. With the application of cross-correlation functions, our results provide two notable findings. First, the return spillovers from the foreign exchange markets to most metal commodity markets are much more striking since the trade war began in 2018 under the former US President Donald Trump. Second, China’s rubber and the US soybean markets during the trade war are found to be dominant sources of volatilities in the USDCNY and CNYUSD exchange markets, respectively. Besides, the effect of shocks in the US soybean return during the trade war seems more measurable for CNYUSD exchange rate risk. To this end, our findings provide insights into the national level of investment and trade policy.

Genetic mapping reveals the complex genetic architecture controlling slow canopy wilting in soybean

In soybean [Glycine max (L.) Merr.], drought stress is the leading cause of yield loss from abiotic stress in rain-fed US growing areas. Only 10% of the US soybean production is irrigated; therefore, plants must possess physiological mechanisms to tolerate drought stress. Slow canopy wilting is a physiological trait that is observed in a few exotic plant introductions (PIs) and may lead to yield improvement under drought stress. Canopy wilting of 130 recombinant inbred lines (RILs) derived from Hutcheson × PI 471938 grown under drought stress was visually evaluated and genotyped with the SoySNP6K BeadChip. Over four years, field evaluations of canopy wilting were conducted under rainfed conditions at three locations across the US (Georgia, Kansas, and North Carolina). Due to the variation in weather among locations and years, the phenotypic data were collected from seven environments. Substantial variation in canopy wilting was observed among the genotypes in the RIL population across environments. Three QTLs were identified for canopy wilting from the RIL population using composite interval mapping on chromosomes (Chrs) 2, 8, and 9 based on combined environmental analyses. These QTLs inherited the favorable alleles from PI 471938 and accounted for 11, 10, and 14% of phenotypic variation, respectively. A list of 106 candidate genes were narrowed down for these three QTLs based on the published information. The QTLs identified through this research can be used as targets for further investigation to understand the mechanisms of slow canopy wilting. These QTLs could be deployed to improve drought tolerance through a targeted selection of the genomic regions from PI 471938.

Comparison of the quality of soybean meal and oil by soybean production origin

AbstractPrevious reports indicate variable soybean quality parameters exported from different geographic regions. This review compares soybean and soybean co‐products grown under diverse environmental conditions. While numerous studies have been conducted on whole soybean and soybean meal (SBM) composition by origin, similar analysis of soybean oil is lacking. This review has two objectives: 1) summarize soybean and SBM quality by origin using a meta‐analysis approach, and 2) analyze collected crude degummed soybean oil samples that originate from the US, Brazil and Argentina for key quality attributes. Soybeans from Brazil have higher levels of protein (P < 0.05) than US soybeans, but US soybeans have lower heat damage (P < 0.05) and total damage (P < 0.05) than soybeans from Brazil. US and Brazil SBM have higher crude protein (CP) (P < 0.05) than SBM from Argentina. At equal CP content, US SBM had less fiber (P < 0.0001), more sucrose (P < 0.0001) and lysine (P < 0.0001) and better protein quality than South American SBMs. Methionine, threonine, and cysteine levels were similar in soybean protein from US and Argentina and higher than that in soybean protein from Brazil. Crude degummed soybean oil from Brazil had more (P < 0.05) free fatty acids, neutral oil loss, phosphorus, calcium and magnesium than crude degummed soybean oil from the US or Argentina. Our analysis suggests that environmental conditions under which soybeans are grown, stored, and handled can have a large impact on chemical composition and nutrient quality of soybean meal and soybean oil.

US-China trade friction and agricultural nitrogen loss in China

Agricultural production, trade and environmental quality are interdependent. The US and China have been the world largest trade partners. However, trade tensions and interest in protectionist policies intensified in recent years. We use a price endogenous partial equilibrium model to systematically examine the impact of increased Chinese tariff on agricultural imports from the US on nitrogen (N) loss in China. The results show that a 25% tariff on US soybean imports increases the acreage of major crops in China by 1.37%. Although aggregate crop acreage increases, the impact on cumulative nitrogen fertilizer uses and the corresponding N loss from cropland is not significant as less N-intensive soybean acreage replaces some of the more N-intensive crops like wheat. However, our estimates show significant heterogeneity in N loss across Chinese provinces, ranging from 0.05% to 2.97% increase. These results identify provinces where remediation measures may be needed to effectively manage the potential increase in nitrogen loss associated with greater soybean production.

Crop Monitoring System Using MODIS Time-Series Data for Within-Season Prediction of Yield and Production of US Corn and Soybeans

In terms of contribution to global food security, this study aimed to build a crop monitoring system for within-season yield prediction of US corn and soybeans by using the Moderate Resolution Imaging Spectroradiometer (time-series data, which consists of three essential core algorithms (crop phenology detection, early crop classification, and crop yield prediction methods)). Within-season predictions for 2018–2022 were then made to evaluate the perfor- mance of the proposed system by comparing it with the United States Department of Agriculture's (USDA's) monthly forecasts and the fixed statistical data. The absolute percentage errors of the proposed system for predicting national-level yield and production were less than 5% for all simulation years as of day of year (DOY) 279. The prediction accuracy as of DOY 247 and DOY 279 were comparable to the USDA's forecasts. The proposed system would enable us to make a comprehensive understanding about overview of US corn and soybean crop condition by visualizing detail spatial pattern of good- or poor harvest regions on a within-season basis.

Leveraging Remote Sensing Data for Yield Prediction with Deep Transfer Learning

Remote sensing data represent one of the most important sources for automized yield prediction. High temporal and spatial resolution, historical record availability, reliability, and low cost are key factors in predicting yields around the world. Yield prediction as a machine learning task is challenging, as reliable ground truth data are difficult to obtain, especially since new data points can only be acquired once a year during harvest. Factors that influence annual yields are plentiful, and data acquisition can be expensive, as crop-related data often need to be captured by experts or specialized sensors. A solution to both problems can be provided by deep transfer learning based on remote sensing data. Satellite images are free of charge, and transfer learning allows recognition of yield-related patterns within countries where data are plentiful and transfers the knowledge to other domains, thus limiting the number of ground truth observations needed. Within this study, we examine the use of transfer learning for yield prediction, where the data preprocessing towards histograms is unique. We present a deep transfer learning framework for yield prediction and demonstrate its successful application to transfer knowledge gained from US soybean yield prediction to soybean yield prediction within Argentina. We perform a temporal alignment of the two domains and improve transfer learning by applying several transfer learning techniques, such as L2-SP, BSS, and layer freezing, to overcome catastrophic forgetting and negative transfer problems. Lastly, we exploit spatio-temporal patterns within the data by applying a Gaussian process. We are able to improve the performance of soybean yield prediction in Argentina by a total of 19% in terms of RMSE and 39% in terms of R2 compared to predictions without transfer learning and Gaussian processes. This proof of concept for advanced transfer learning techniques for yield prediction and remote sensing data in the form of histograms can enable successful yield prediction, especially in emerging and developing countries, where reliable data are usually limited.

Evaluating the Effects of RA on the Rheological Properties and Aging Susceptibility of RAM Asphalt

Recycling agents (RAs) can mitigate the adverse effects of reclaimed asphalt pavement (RAP) on recycled asphalt, which can result in more RAP being added. To explore the effects of four RAs on recycled asphalt, this study used asphalt rheological performance experiments, including the bending beam rheometer (BBR) test, the dynamic shear rheological (DSR) test, and the indirect tensile asphalt cracking test (IDEAL-CT), to study the performance grade (PG), cracking resistance, and cracking susceptibility of recycled asphalt. In addition, an aging model for asphalt was used, and short-term and long-term aging sensitivities were evaluated according to this model. Results showed that US Soybean significantly enhanced the high-temperature and low-temperature performance grade (PGLT) and cracking resistance and reduced cracking sensitivity of the recycled asphalt. In addition, the short-term aging sensitivity was the lowest. It showed the best regeneration effect. However, the recycled asphalt with US Soybean showed the highest long-term aging sensitivity, suggesting that longer aging time results in poorer performance. Ingevity’s PG, cracking resistance, and cracking sensitivity are slightly lower than that of US Soybean, and Ingevity demonstrated the second lowest short-term aging sensitivity. However, its prolonged aging sensitivity was much lower than that of US Soybean. Asphalt and Wax Innovations and Georgia Pacific agents slightly improved recycled asphalt properties. The long-term aging sensitivity of recycled asphalt with four Ras was higher than that of recycled asphalt without Ras. These results indicated that the recycled asphalt’s performance with Ras worsened as aging time increased compared to the original recycled asphalt.

Mapping variability of soybean leaf potassium concentrations to develop a sampling protocol

AbstractThe spatial variability of soybean [Glycine max (L.) Merr.] leaf‐K (potassium) concentrations must be considered when collecting samples to monitor crop K status. Five commercial soybean fields were sampled at a 0.4‐ha grid resolution at two reproductive growth stages to quantify the trifoliolate tissue‐K concentration. The objectives of this study were to identify the potential field variability of soybean leaf‐K concentrations in typical Mid‐south US soybean production fields, evaluate interpolation methods, and develop a sampling protocol for in‐season soybean tissue monitoring. No consistent spatial dependencies were found in the leaf‐K concentrations across the fields and sample times, indicating that a soybean tissue‐K grid sampling protocol cannot be generalized to a specific area size. Inverse distance weighted (IDW) and rasterization interpolation methods were considered to predict leaf‐K concentrations between the sampled grid points at grid resolutions ranging from 0.4 to 4 ha. The IDW method consistently predicted leaf‐K concentrations between the known values with less error than rasterization. Rather than grid sampling, composite leaf samples should be collected based on management zones to provide a simplified sampling protocol. Within each management zone, a composite sample must consist of uppermost fully expanded trifoliolate leaves collected from at least 18 locations to ensure that the sample measures within the 95% confidence interval of the area average leaf‐K concentration. The developed sampling protocol coupled with the dynamic critical tissue‐K concentration curve will provide producers with the ability to effectively monitor soybean for potential hidden hunger and verify K deficiency symptoms in season.

THE IMPACT OF IMPORT LIBERALIZATION POLICY ON INDONESIAN SOYBEAN IMPORTS

Indonesian soybean consumption is on the rise, meanwhile soybean production is decreasing, leading to a continuous increase in imports to meet the growing demand. This study aims to (1) assess the trend of soybean imports in Indonesia and (2) identify the factors influencing soybean imports in the country. The research relies on secondary data in the form of time series data spanning from 1991 to 2020. The fundamental methodology employed in this study is quantitative descriptive analysis. The analytical approach adopted is ordinary least squares (OLS), utilizing simple linear regression to discern the trend in soybean imports and multiple linear regression to pinpoint the factors impacting soybean imports. The findings reveal that Indonesian soybean imports exhibit an average annual increase of 72,628.82 tons. Partially, GDP demonstrates a positive and significant influence on the volume of Indonesia's soybean imports. The volume of soybean imports in Indonesia posts the implementation of the import liberalization policy in 1998 surpassed pre-policy levels. Conversely, the exchange rate of the United States Dollar against the Indonesian Rupiah has a negative and significant impact. Additionally, soybean productivity and US soybean producer prices have an insignificant effect on the volume of Indonesian soybean imports.

Cross‐border and cross‐commodity volatility spillover effects of Chinese soybean futures

AbstractThis study examines the volatility spillover between the US and China's soybean futures markets, and between China's soybean, soybean oil, and meal futures markets. A synchronization technique is used to overcome the bias from differences in the closing time of two exchanges. On the basis of BEKK‐GARCH and DCC‐GARCH on the data from January 2010 to April 2023, our results indicate that: First, there is a one‐way spillover from the US to China's No.1 soybean futures and a two‐way spillover between the US and No.2 soybean futures. Second, after 2018, the volatility spillovers of No.1 soybean futures declined, while that of No.2 increased significantly. Third, the dynamic conditional correlations confirmed our results that the spillover between No.2 soybean and soymeal, and between No.2 soybean and soyoil futures increased significantly. Our findings demonstrate the leading role of US soybeans in cross‐border trading and the increasing role of No.2 soybeans in cross‐country and cross‐commodity spillover.

Brazilian soybean expansion, US–China trade war, and US soybean exports

AbstractThis paper estimates the impact of Brazilian soybean area expansion on US and Brazilian soybean exports to China by projecting these countries’ exports for various Brazilian soybean area growth scenarios. The results indicate that the Brazilian area expansion does not significantly affect US soybean exports to China but positively impacts Brazil's soybean exports to China. Ex‐post analysis of the impacts of Chinese tariff during the US–China trade war indicates that China significantly diverted its imports from the United States to Brazil. We conclude that the US soybean trade policy should focus on mitigating the adverse effects of import demand changes.

Parental choice and seed size impact the uprightness of progeny from interspecific Glycine hybridizations

AbstractThe narrow genetic base of the US soybean (Glycine max [L.] Merr.) crop makes it vulnerable to emerging abiotic and biotic stress challenges, and limits resiliency of the soybean crop to meet changes in consumer demand for improved seed composition and agronomic performance. The United States Department of Agriculture Glycine soja (Siebold & Zucc.) germplasm collection provides a valuable genetic resource to meet these challenges. Glycine soja is more genetically diverse than domesticated soybean. Even though wild soybean hybridizes freely with G. max, breeding efforts with wild soybean have not been widely employed because the interspecific progeny inherits undesirable traits from the wild parent. Particularly, these progenies inherit a vine‐like architecture that prevents machine harvest. We assessed the potential for improving recovery of agronomically valuable progeny from interspecific crosses in three experiments using 11 interspecific populations. We found that optimal choice of either parent could triple the frequency of recovery of desirable progeny. Selection for large F3 seed size was positively correlated with upright growth habit/plant architecture in five of 11 populations and could triple the recovery of desirable plants. Marker analysis of parents and progeny indicated selection for larger seed size caused minimal selection against the genome of the wild parent. Most genetic markers specific to a wild parent could be recovered in the aggregate of 8–10 upright interspecific progenies, identified via post selection marker analysis. We concluded that choice of parent and selection for larger seed can maximize recovery of the wild genome in machine harvestable progenies.

Switching costs in the US seed industry: Technology adoption and welfare impacts

We evaluate the role of brand and technology switching costs in the US soybean seed industry using a unique dataset of actual seed purchases by about 28,000 farmers from 1996 to 2016. Using a random coefficients logit model of demand, we estimate brand and technology switching costs, characterize the distributions of buyers’ willingness to pay for seed brands and the glyphosate tolerance (GT) trait, and assess the implications of brand and technology switching costs for farmers’ welfare, technology adoption, firm profits, and firm market shares. We find that farmers are willing to pay large premiums for brand labels, and even larger premiums for the GT trait, although there is considerable heterogeneity in these values. Switching costs play an important role in the soybean seed industry. Eliminating these costs would significantly increase buyers’ welfare, reduce seed prices and firm profits, decrease adoption of the GT trait, and impact industry consolidation by expanding smaller firms’ market shares.

The impact of Sino–US trade war on price discovery of soybean: A double‐edged sword?

AbstractIn 2018, the world witnessed a full‐fledged trade war between United States and China with both countries levying successive tariffs on each other. Though the impact of trade war could be visibly assessed from declining export volume and rising bankruptcy filing from farmers, what went unnoticed was a far‐reaching negative impact at a granular market microstructure level. In this study, the effect of trade war on the leading role of US soybean futures in price discovery has been evaluated. The results corroborate the negative impact of trade war on the information leadership share of US soybean futures market. Moreover, the results shows that the negative impact gets manifested through various sentiment indicators such as Google search trends, hedger's sentiment, and so on. Hence, trade war may have given some temporary economic and strategic benefit to the United States, but it is no less than the proverbial double‐edged sword as it nearly killed the soybean market of the United States.

Market reactions to trade friction between China and the United States: Evidence from the soybean futures market

In March 2018, the US used an immense trade deficit as an excuse to provoke trade friction with China. This study uses the EGARCH model and event study methods to study the impact of the major risk event of Sino-US trade friction on soybean futures markets in China and the United States. Results indicate that the Sino-US trade friction weakened the return spillover effect between the soybean futures markets in China and the US, and significantly increased market volatilities. As the scale of additional tariffs increased, the volatility of the Chinese soybean futures market declined; however, the volatility of the US soybean futures market did not weaken. In addition, expanding the sources of soybean imports helped ease the impact of tariffs on China’s soybean futures market, while the decline in US soybean exports to China intensified the volatility of the US soybean futures market. In addition, while the release of multiple tariff increases has had a short-term impact on the returns of soybean futures markets, the impact of trade friction has gradually decreased.