World Soybean Research Articles

Comparative effectiveness of Metarhizium rileyi, novaluron, and glyphosate on immune system, development, and redox metabolism of Anticarsia gemmatalis

Anticarsia gemmatalis is one of the most important pests in world soybean crop. The most common intervention is the application of agrochemicals, such as novaluron and glyphosate. Among biological control agents, much attention has been drawn to entomopathogenic fungi, as Metarhizium rileyi. Here, we examined the changes that occur in the immune system (total and differential hemocyte count), secondary effects (caterpillar morphology), and oxidative metabolism after the caterpillars were exposed to M. rileyi, novaluron or glyphosate. M. rileyi was able to induce changes in the width, length, and weight of A. gemmatalis pupae, along with an increased in the number of defense cells. Novaluron prompt changes the insect’s immunity, and glyphosate caused milder immunological effects. However, it caused significant secondary effects including malformations in pupae and adults, and an increase in nitric oxide (NO) levels. Mortality observed when treating insects with novaluron and malformations due to glyphosate treatments did not occur due to oxidative stress. However, when insects were exposed to M. rileyi, we verified significantly increased levels of NO and concluded that these insects died due to oxidative stress. Our data provide evidence that contributes to better understanding the mechanism of herbicide-fungus interaction in the management of Anticarsia gemmatalis.

Assessment of Climate Change by Crop Yields

With the United States producing 41% of the world's maize and 38% of the world's soybeans, the impact on US crop yields has implications for world food supply. We combine the US state results committee with a detailed climate information index that integrates the entire cycle between baseline and most extreme temperatures each day and throughout the day of the development season. The temperature rises to around 29 ° C for corn, 30 ° C for soybeans, and 32 ° C for cotton, but temperatures above this limit can be devastating. The slope of the attenuation towards the full ideal is more extreme than the slope below it. Similar non-linear and aberrant relationships can be seen taking into account the temporal arrangement of climate and yield or cross-sectional diversity. This suggests the possibility of limited variability in plant species given the fact that the last crop introduced the conversion of the farmer to a warmer environment, whereas the previous crop did not ... Considering current developing regions, normal yields by zone are expected to fall by 31-43% under slowing warming and 67-79% at the fastest-warming conditions before the end of this century.

Impacts of agricultural price support policy on price variability and welfare: Evidence from China's soybean market

AbstractChina is a predominant soybean importer; thus, its domestic soybean policies have a large impact on world soybean market dynamics. We develop the first aggregate structural econometric model of China's soybean market and link it to the rest of the world (ROW), which allows us to analyze the impacts of China's soybean price support policy that ran from 2008 to 2013. We investigate the impacts of China's policy on the variability of their domestic and world soybean prices, and adopt a Monte Carlo simulation to evaluate the effects on distributional and aggregate welfare. Our results indicate: (a) after China's soybean price support policy, price transmission elasticity decreases, China's domestic price variability decreases, and world price variability increases; (b) China's producer surplus increases, consumer surplus decreases and the net welfare change in their domestic market is negative; and (c) although soybean exporting countries in the ROW experience significant welfare gains, the world net welfare change is negative.

World soybean market: trends in the development of international trade

World soybean market: trends in the development of international trade

Productivity of soybean varieties under different irrigation regimes

Abstract. World soybean acreage increases by 3 million hectares annually with average yield of 2.7 t/ha. Significant growth of soybean production in Russia is constrained by increased climate aridization and a declining yield of up to 1.5 t/ha. An important factor in intensification of soybean production is to expand its crops under irrigation. Introduction of adapted and high-yielding varieties of regional selection, followed by improvement of crop water supply, increases yields up to 34 t/ha. Soybean varieties selected by Russian Research Institute of Irrigated Agriculture and admitted to production in the Lower Volga region: VNIIOZ 86 (since 2002), VNIIOZ 31 (since 2011), Volgogradka 2 (since 2020) were studied. The experiments were conducted at Russian Research Institute of Irrigated Agriculture in 2013-2015. The experiment included two factors: factor A - varieties, factor B - irrigation regime (70-80-70 % of FMC, 80-80-70 % of FMC and control - 80-80-80 % of FMC). Plots of the 1st (600 m2) and 2nd (200 m2) order were sown in 4-fold replication by a wide-row method (0.7 m) in mid-May with a planned yield of 2.53.5 t/ha (N90P90K60 a. i./ha). Soybean varieties differed in peculiarities of crop structure formation. Differentiated irrigation regime resulted in more cost-efficient water consumption followed by yield increase compared to the control. The highest yields were formed by Volgogradka 2 variety (2.873.23 t/ha) and VNIIOZ 31 (2.823.19 t/ha), which was significantly higher than in VNIIOZ 86 variety (2.172.51 t/ha). The variable irrigation regime led to yield increase in all soybean varieties, especially in Volgogradka 2 - by 0.310.36 t/ha (10.812.5 %) as compared to the control. It was due to grain increase to 30.936.2 % in the total biomass compared to the control values - 26.627.5 %. The highest amount of post-harvest plant residues (stems, leaves and roots) remained after harvesting Volgogradka 2 (6.397.63 t/ha) and VNIIOZ 31(6.737.9 t/ha), which improved soil fertility well, and the smallest amount was after VNIIOZ 86 variety (4.415.66 t/ha). Differentiated irrigation regime led to decrease in vegetative mass in soil - 4.417.42 t/ha compared to the control (5.667.9 t/ha). Thus, Volgogradka 2 and VNIIOZ 31 can be recommended for irrigated agriculture in the Lower Volga region, as they provide high yields under differentiated (relatively cost-efficient) irrigation regime and improve soil fertility due to large biomass remained in soil after harvesting.

Time Series Prediction with Artificial Neural Networks: An Analysis Using Brazilian Soybean Production

Food production to meet human demand has been a challenge to society. Nowadays, one of the main sources of feeding is soybean. Considering agriculture food crops, soybean is sixth by production volume and the fourth by both production area and economic value. The grain can be used directly to human consumption, but it is highly used as a source of protein for animal production that corresponds 75% of the total, or as oil and derived food products. Brazil and the US are the most important players responsible for more than 70% of world production. Therefore, a reliable forecasting is essential for decision-makers to plan adequate policies to this important commodity and to establish the necessary logistical resources. In this sense, this study aims to predict soybean harvest area, yield, and production using Artificial Neural Networks (ANN) and compare with classical methods of Time Series Analysis. To this end, we collected data from a time series (1961–2016) regarding soybean production in Brazil. The results reveal that ANN is the best approach to predict soybean harvest area and production while classical linear function remains more effective to predict soybean yield. Moreover, ANN presents as a reliable model to predict time series and can help the stakeholders to anticipate the world soybean offer.

A study of the genetic diversity in the world soybean collection using microsatellite markers associated with fungal disease resistance

Background.Soybean (Glycine max (L.) Merr.) gradually becomes one of the leading legume crops in Kazakhstan. The area under soybeans in the country has been increasing annually and requires the development of adapted cultivars with a higher yield, improved quality characters, and resistance to emerging fungal diseases. The enlargement of the crop’s gene pool also suggests the need to study and document local soybean accessions to meet the standards of the available world soybean collection by using reliable and informative types of DNA markers.Materials and methods. In this study, the soybean collection consisting of 288 accessions from different countries, including 36 cultivars and promising lines from Kazakhstan, was studied. The molecular genetic analysis was performed using nine polymorphic SSR (simple sequence repeats) markers, seven of which (Satt244, Satt565, Satt038, Satt309, Satt371, Satt570 and Sat_308) were associated with resistance to three main fungal diseases of soybean – frogeye leaf spot, fusarium root rot, and purple seed stain.Results. The average PIC (polymorphism information content) value of the analyzed SSR markers constituted 0.66 ± 0.07, confirming their highlevel polymorphism. The principal coordinate analysis suggested that the local accessions were genetically most close to the accessions from East Asia. As the collection showed a robust resistance to three studied fungal diseases in Almaty Region during 2018–2019, the distribution of the studied SSR markers in the population was not significantly associated with resistance to the analyzed diseases under field conditions.Conclusion. SSR genotyping of the soybean collection helped to identify accessions that potentially possess resistance-associated alleles of fungal disease resistance genes. The data obtained can be further used for the development of DNA documentation and the breeding the promising cultivars and lines of soybean.

Effects of the El Niño Southern Oscillation phenomenon and sowing dates on soybean yield and on the occurrence of extreme weather events in southern Brazil

ABSTRACT Brazil accounts for 32% of world soybean production and potential yield losses related to climate variability in the country can have an adverse effect on global supply. Recent soybean production declines in Brazil (e.g. The 15.6% soybean yield drop occurred during the 2011/12 growing season) were a consequence of extreme weather events caused by the effects of the El Nino Southern Oscillation (ENSO) phenomenon. ENSO is one of the most important atmospheric-oceanic phenomena responsible for climate and crop yield variability in several agricultural producing regions of Brazil. The greatest challenge related to adapting to ENSO climate cycles is to understand how to minimize the negative impacts and take advantage of favorable conditions to improve yields. Thus, the aim of this study was to determine the spatial and temporal impacts of sowing dates and ENSO phases on soybean yield and the frequency of extreme weather events (high, low temperature and dry period) during the vegetative and reproductive soybean phenological phases in southern Brazil. For that, soybean yields for 16 different sowing dates (from September to February) were simulated using a well-calibrated crop growth and development model (CROPGRO-Soybean) for 12 locations across southern Brazil and considering historical weather series of 56 years (1961-2016). Homogeneous regions related to ENSO impacts were defined by cluster analysis to facilitate the understanding and use of the information produced. Alternative ENSO phases were found to affect the frequency of extreme weather events and soybean yields across the region studied. Potential negative ENSO effects could be minimized by adjusting the sowing windows according to the expected climate cycle. Our results revealed that ENSO impacts on soybean yields in southern Brazil vary according to the location and can be categorized into three main groups: Group I, where the impacts of the El Nino phase on soybean yield tend to be positive, and its positive impact did not vary with the different sowing dates; Group II, where the negative soybean yield anomalies were more frequent in Neutral years; and Group III, where the positive and negative impacts of ENSO phases varied with sowing dates.

The Impacts of Soy-Based Biodiesel on the Main Soy Producers in the International Market

This study analyzes the impacts of soy-based biodiesel consumption on the main soybean-producing countries. To achieve this goal, a partial equilibrium model formulated as a Mixed Complementarity Problem (PCM) is used, which allows the inclusion of trade barriers and the construction of alternative scenarios. Four scenarios are analyzed. The first simulates a demand shock for soybean meal and soybean oil, considering the observed growth in demand over the last ten years. The second evaluates the effects of an increase in the demand for meal, oil and biodiesel. The third considers the elimination of the demand for soy-based biodiesel. The last scenario assesses the effects of the elimination of tariffs and subsidies on the grain and food markets. In general terms, the results show that: in the second scenario, Brazil is the only exporting country that shows a greater increase in consumption than in production, reflecting the country’s smaller share in the world soybean trade; in the fourth scenario the gains are relatively smaller than the potential gains in the first scenario; in the third scenario, soybean prices are about 8% higher because of the use of soybeans for biodiesel production, demonstrating the relevance of mandatory policies in this market.

Natural Variation in Fatty Acid Composition of Diverse World Soybean Germplasms Grown in China

Soybean (Glycine max L. Merr.) is one of the most important crops in the world. Its major content of vegetable oil made it widely used for human consumption and several food industries. To investigate the variation in seed fatty acid composition of soybeans from different origins, a set of 633 soybean accessions originated from four diverse germplasm collections—including China, United States of America (USA), Japan, and Russia—were grown in three locations, Beijing, Anhui, and Hainan for two years. The results showed significant differences (P < 0.001) among the four germplasm origins for all fatty acid contents investigated. Higher levels, on average, of palmitic acid (PA) and linolenic acid (LNA) were observed in Russian germplasm (12.31% and 8.15%, respectively), whereas higher levels of stearic acid (SA) and oleic acid (OA) were observed in Chinese germplasm (3.95% and 21.95%, respectively). The highest level of linoleic acid (LA) was noticed in the USA germplasm accessions (56.34%). The largest variation in fatty acid composition was found in LNA, while a large variation was observed between Chinese and USA germplasms for LA level. Maturity group (MG) significantly (P < 0.0001) affected all fatty acids and higher levels of PA, SA, and OA were observed in early maturing accessions, while higher levels of LA and LNA were observed in late maturing accessions. The trends of fatty acids concentrations with different MG in this study further provide an evidence of the importance of MG in breeding for such soybean seed components. Collectively, the unique accessions identified in this study can be used to strengthen the soybean breeding programs for meeting various human nutrition patterns around the globe.

Geographic differentiation and phylogeographic relationships among world soybean populations

A fast-growing protein and oil crop, soybean was domesticated in ancient China and disseminated early in Asia and afterwards to other continents, in particular the Americas in recent centuries. After adaptation, locally developed landraces and cultivars formed a diversity of geographic-populations. In an investigation of their phylogeographic features, marker-derived traits were combined with geography-related photo- and temperature-sensitive traits to study 13 geographic-populations comprising 371 accessions. Extreme differentiation among geographic-populations was observed for flowering date (33–94 days), maturity date (79–181 days), and main stem node number (6–25 nodes). Restriction-site associated DNA sequencing revealed strong genetic differentiation among these geographic-populations, including genetic richness (alleles, 35,242–44,986) and specific-present alleles (SPAs, 0–67). More SPAs (28–67) emerged in some secondary and tertiary centers than in centers of origin (8–11). Phenotypic and genotypic clustering divided 11 of the 13 geographic-populations into the same five sets of sensitivity-similar geographic-populations and grouped the populations of northeast China and northern North America rather than center-of-origin populations as secondary centers, indicating the importance of geography-related traits in determining genetic differences among geographic-populations. A model of four soybean dissemination paths is presented: from the center of origin to the north, east, and south in Asia and from northeast China to Europe and the Americas. These findings provide a detailed phylogeographic understanding of worldwide soybeans.

The Introduction of Thousands of Tonnes of Glyphosate in the food Chain-An Evaluation of Glyphosate Tolerant Soybeans.

Glyphosate-tolerant (GT) soybeans dominate the world soybean market. These plants have triggered increased use of, as well as increased residues of, glyphosate in soybean products. We present data that show farmers have doubled their glyphosate applications per season (from two to four) and that residues of late season spraying of glyphosate (at full bloom of the plant) result in much higher residues in the harvested plants and products. GT soybeans produced on commercial farms in the USA, Brazil and Argentina accumulate in total an estimated 2500–10,000 metric tonnes of glyphosate per year, which enter global food chains. We also review studies that have compared the quality of GT soybeans with conventional and organic soybeans. Feeding studies in Daphnia magna have shown dose-related adverse effects (mortality, reduced fecundity and delayed reproduction) of glyphosate residues in soybeans, even at glyphosate concentrations below allowed residue levels. We argue that GT soybeans need to be tested in fully representative and realistic contexts. However, the current risk assessment system has only required and received data from field trials with beans that were sprayed with much lower doses of glyphosate as compared to contemporary commercial farms. This has left knowledge gaps and a potentially serious underestimation of health risks to consumers.

Soybean Price Pattern Discovery Via Toeplitz Inverse Covariance-Based Clustering

The high volatility of world soybean prices has caused uncertainty and vulnerability particularly in the developing countries. The clustering of time series is a serviceable tool for discovering soybean price patterns in temporal data. However, traditional clustering method cannot represent the continuity of price data very well, nor keep a watchful eye on the correlation between factors. In this work, the authors use the Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data (TICC) to soybean price pattern discovery. This is a new method for multivariate time series clustering, which can simultaneously segment and cluster the time series data. Each pattern in the TICC method is defined by a Markov random field (MRF), characterizing the interdependencies between different factors of that pattern. Based on this representation, the characteristics of each pattern and the importance of each factor can be portrayed. The work provides a new way of thinking about market price prediction for agricultural products.

Seasonal Soybean Price Transmission between the U.S. and Brazil Using the Seasonal Regime-Dependent Vector Error Correction Model

Soybean production and trade in the U.S. and Brazil are seasonal. Our research question is whether the seasonal tendencies cause the price relationship between U.S. and Brazilian soybean prices. Therefore, the objective is to test for seasonality in the price transmission between the U.S. and Brazil soybean prices using the seasonal regime-dependent vector error correction model (VECM). Our results show that the speed of the adjustment for the U.S. soybean price in the first half of the year is greater than the speed of the adjustment for the Brazilian soybean price. However, the pattern of their responses becomes the reverse in the second half of the year. The component share calculated by the result of the VECM with seasonal effects indicates that the U.S. dominates the world soybean market during the second half of the year while Brazil is dominant in the soybean market in the first half of the year. These results give us an important finding that we could not find using the VECM without seasonal effects. Finally, our results imply that the seasonal pattern of production in the U.S. and Brazil could cause the sustainability of the supply chain in the world soybean market.

FAKTOR – FAKTOR YANG TERKAIT DENGAN EKSPOR CRUDE PALM OIL (CPO)

This study aims to analyze the balance of long-term and error term, CPO export prices, world soybean oil prices, domestic CPO production, inflation, exchange rate, export tax applicable to the export volume of CPO. The analytical method used Engle Granger cointegration analysis,Error Correction Model (ECM) and completed with E-Views. The research data is secondary weeklydata from year 2006 - 2015. The data is analyzed by Engel Granger cointegration and Error Correction Modelmethod. The result of the analysis shows that there is a positive correlation between CPO export price, soybean oil and domestic CPO to export of CPO. While the inflation, exchange rate, and export tax variables show a negative correlation to CPO exports. In estimation of error-term, there is a positive correlation to CPO export price variables, soybean oil price and CPO domestic production. While the variables of inflation, exchange rate and export tax show negative correlation to CPO exports.

Agrobacterium rhizogenes-induced soybean hairy roots versus Soybean mosaic virus (ARISHR-SMV) is an efficient pathosystem for studying soybean\u2013virus interactions

BackgroundSoybean mosaic virus (SMV), a Potyvirus, is the most prevalent viral pathogen of soybean that causes severe yield and seed quality reductions in world soybean production. So far, multiple resistance loci for different SMV strains have been fine-mapped while the candidate genes’ functions need to be verified. However, identification of the resistance or susceptibility genes via stable genetic transformation is time-consuming and labor-intensive, which hinders further exploration of these genes’ functions in soybean. Thus we tried to explore a rapid and efficient method for verification of SMV-related target gene function in soybean.ResultsAn Agrobacterium rhizogenes (A. rhizogenes) induced soybean hairy roots versus Soybean mosaic virus (ARISHR-SMV) pathosystem was established. The procedure is characterized with that (1) the soybean hairy roots that can be infected by SMV are induced by A. rhizogenes K599 using soybean cotyledons as explants, (2) the gene to be examined for its function, which may be the endogenous SMV-resistance or -susceptible gene or exogenous SMV-related gene, is transformed into soybean calluses mediated by A. rhizogenes, (3) the transformed calluses on explants further inoculated with the purified tester-SMV virions using pricking method under aseptic conditions, and (4) the measurement of the SMV content in positive hairy roots for evaluating the SMV-related target gene function. The procedure takes about 30 days for one cycle. Utilizing the established procedure, the soybean hairy roots induced by A. rhizogenes was efficiently infected by multiple different SMV strains, the SMV infectivity in soybean hairy roots can be retained at least twice successive transfer cultures and Tomato bushy stunt virus (TBSV) P19 promoting and SMV CP suppressing SMV infection in soybean hairy roots was demonstrated, respectively. This procedure can also be used for identification of resistance to SMV strains for soybean germplasms.ConclusionThe ARISHR-SMV is an efficient pathosystem that allows a quick and convenient identification of SMV-related target gene function in soybean.

Soybean-maize off-season double crop system in Brazil as affected by El Niño Southern Oscillation phases

El Niño Southern Oscillation (ENSO) is one of the most important atmospheric-oceanic phenomena, responsible for climate variability in several Brazilian regions, which affects agriculture, mainly soybean – maize off-season succession. Therefore, the ENSO impacts on soybean – maize off-season double crop system can affect global food security, since Brazil is a major player as producer of these two crops, with a total production that represents 27% and 6% of world's soybean and maize production, respectively. In order to understand the risks associated to this crop system, the aim of this study was to assess the influence of ENSO phenomenon on the spatial and temporal soybean and maize off-season yield variabilities, considering simulations with three different crop models (FAO-AZM, DSSAT and APSIM) in a multi-model approach, and to determine the best sowing windows for this production system for each ENSO phase (El Niño, La Niña and Neutral) in different Brazilian producing regions. Previously calibrated and validated models were used to simulate soybean yields for 29 locations in 12 states, with sowing dates ranging from late September to early January of each growing season for a period of 34 years (1980–2013). The maize off-season sowing was done just after the soybean harvest, ranging from late January to early May. ENSO phases affected soybean and maize yields across the country, which can be minimized by choosing the best sowing window for soybean. In northern Brazil, El Niño negatively impacts soybean and maize off-season yields, making the succession of these crops risk, with the best sowing window being very short. Similar result was found for southern and central Brazil during La Niña years. On the contrary, cropping soybean and maize off-season in succession during El Niño years in center-south of and during La Niña years up north have higher chances of success.

2018 World Soybean Trade: Aberrations from Recent Trends

2018 World Soybean Trade: Aberrations from Recent Trends

Analysis of QTL-allele system conferring drought tolerance at seedling stage in a nested association mapping population of soybean [Glycine max (L.) Merr.] using a novel GWAS procedure.

RTM-GWAS identified 111 DT QTLs, 262 alleles with high proportion of QEI and genetic variation accounting for 88.55-95.92% PV in NAM, from which QTL-allele matrices were established and candidate genes annotated. Drought tolerance (DT) is one of the major challenges for world soybean production. A nested association mapping (NAM) population with 403 lines comprising two recombinant inbred line (RIL) populations: M8206 × TongShan and ZhengYang × M8206 was tested for DT using polyethylene-glycol (PEG) treatment under spring and summer environments. The population was sequenced using restriction-site-associated DNA sequencing (RAD-seq) filtered with minor allele frequency (MAF) ≥ 0.01, 55,936 single nucleotide polymorphisms (SNPs) were obtained and organized into 6137 SNP linkage disequilibrium blocks (SNPLDBs). The restricted two-stage multi-locus genome-wide association studies (RTM-GWAS) identified 73 and 38 QTLs with 174 and 88 alleles contributed main effect 40.43 and 26.11% to phenotypic variance (PV) and QTL-environment interaction (QEI) effect 24.64 and 10.35% to PV for relative root length (RRL) and relative shoot length (RSL), respectively. The DT traits were characterized with high proportion of QEI variation (37.52-41.65%), plus genetic variation (46.90-58.40%) in a total of 88.55-95.92% PV. The identified QTLs-alleles were organized into main-effect and QEI-effect QTL-allele matrices, showing the genetic and QEI architecture of the three parents/NAM population. From the matrices, the possible best genotype was predicted to have a weighted average value over two indicators (WAV) of 1.873, while the top ten optimal crosses among RILs with 95th percentile WAV 1.098-1.132, transgressive over the parents (0.651-0.773) but much less than 1.873, implying further pyramiding potential. From the matrices, 134 candidate genes were annotated involved in nine biological processes. The present results provide a novel way for molecular breeding in QTL-allele-based genomic selection for optimal cross selection.

A cointegration analysis of crude palm oil price in Thailand

Reliazing the pass-through effects of global commodity prices on domestic prices, this study develops a vector error correction model (VECM) to test for the determinants and direction of causality between global prices and crude palm oil (CPO) price in Thailand. Malaysian crude palm oil, world soybean oil and world crude oil prices were investigated as factors affecting the Thai CPO price. Using the Johansen cointegration test, the result unveils a presence of long-run relationship among the determinants. This long-run relationship, proposes that CPO price flows in Thailand are positively influenced by the Malaysian CPO price and the error correction term suggests that approximately 35 percent of total disequilibrium in Thai CPO price was corrected in the following month. Moreover, the findings show Granger causality from each of the Malaysian CPO price and the world soybean oil price for the Thai CPO price. Information flow regarding the price movements of the Malaysian CPO and soybean oil affect the Thai CPO price and vice-versa. Whereas, the evidence for a causal relationship that runs from the world crude oil price to the Thai CPO price is found, but not in reverse.