Climate and Management Factors Influence Soybean Yield Potential in a Subtropical Environment

2016 ◽  
Vol 108 (4) ◽  
pp. 1447-1454 ◽  
Author(s):  
Alencar Junior Zanon ◽  
Nereu Augusto Streck ◽  
Patricio Grassini
Keyword(s):  
Yield Potential ◽  
Soybean Yield ◽  
Management Factors ◽  
Subtropical Environment

scholarly journals Optimum Leaf Area Index to Reach Soybean Yield Potential in Subtropical Environment

2018 ◽  
Vol 110 (3) ◽  
pp. 932-938 ◽  
Author(s):  
Eduardo Lago Tagliapietra ◽  
Nereu Augusto Streck ◽  
Thiago Schmitz Marques da Rocha ◽  
Gean Leonardo Richter ◽  
Michel Rocha da Silva ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Yield Potential ◽  
Area Index ◽  
Soybean Yield ◽  
Subtropical Environment

scholarly journals Responses of Branch Number and Yield Component of Soybean Cultivars Tested in Different Planting Densities

Agriculture ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 69
Author(s):  
Cailong Xu ◽  
Ruidong Li ◽  
Wenwen Song ◽  
Tingting Wu ◽  
Shi Sun ◽  
...  
Keyword(s):  
Key Management ◽  
Management Practices ◽  
Yield Component ◽  
Planting Density ◽  
Yield Potential ◽  
High Yield ◽  
Branch Number ◽  
Soybean Yield ◽  
Soybean Cultivars ◽  
Pod Number

Increasing planting density is one of the key management practices to enhance soybean yield. A 2-yr field experiment was conducted in 2018 and 2019 including six planting densities and two soybean cultivars to determine the effects of planting density on branch number and yield, and analyze the contribution of branches to yield. The yield of ZZXA12938 was 4389 kg ha−1, which was significantly higher than that of ZH13 (+22.4%). In combination with planting year and cultivar, the soybean yield increased significantly by 16.2%, 31.4%, 41.4%, and 46.7% for every increase in density of 45,000 plants ha−1. Yield will not increase when planting density exceeds 315,000 plants ha−1. A correlation analysis showed that pod number per plant increased with the increased branch number, while pod number per unit area decreased; thus, soybean yield decreased. With the increase of branch number, the branch contribution to yield increased first, and then plateaued. ZH13 could produce a high yield under a lower planting density due to more branches, while ZZXA12938 had a higher yield potential under a higher planting density due to the smaller branch number and higher tolerance to close planting. Therefore, seed yield can be increased by selecting cultivars with a little branching capacity under moderately close planting.

scholarly journals Model Simulasi dan Visualisasi Prediksi Potensi Hasil dan Produksi Kedelai di Jawa Timur

2016 ◽  
Vol 34 (3) ◽  
pp. 195
Author(s):  
Bambang S. Koentjoro ◽  
Imas S. Sitanggang ◽  
Abdul Karim Makarim
Keyword(s):  
Simulation Model ◽  
Systems Approach ◽  
Yield Potential ◽  
Soybean Plant ◽  
Climatic Data ◽  
Observation Data ◽  
Potential Yield ◽  
Soybean Yield ◽  
Soybean Production ◽  
Growth Simulator

<p>The prediction of national soybean yield and production could be improved its accuracy by integrating a simulation model and Geographic Information Systems (GIS). The objective of this research was to integrate a simulation model with a GIS, to predict the potential yield and production of soybean in the soybean production centers of East Java. This study was conducted from December 2013 till May 2014. The approach used in this study was a systems approach using a simulation model as solution to the problem. The model is SUCROS.SIM (Simple Universal Crops Growth Simulator), which was written using Powersim software and Spreadsheet in order to be fully integrated with GIS. The initial phase of the integration process between SUCROS.SIM and GIS are as follows (a) model validation, using input data of soybean plant assimilate partitioning, (b) climatic data (solar radiation, maximum and minimum temperatures) collected from the climatological station (BMKG) Karangploso Malang and (c) observation data of soybean yields of two varieties (Wilis and Argomulyo) at Muneng Experiment Station. It was found that the coefficients of determination of simulation model of soybean yield potential (R2) range from 0.945-0.992 and RMSE (Root Mean Square Error) values range from 0.11 to 0.25 t/ha. The average of soybean yield potential and production in 2012 at soybean production centers of East Java were 1.94 t/ha and 293,459 ton, respectively. The conclusion is SUCROS.SIM valid to be integrated with GIS.</p>

Suppression of Soybean Yield Potential in the Continental United States by Plant Diseases from 2006 to 2009

2010 ◽  
Vol 11 (1) ◽  
pp. 5 ◽  
Author(s):  
Stephen R. Koenning ◽  
J. Allen Wrather
Keyword(s):  
United States ◽  
The United States ◽  
Plant Diseases ◽  
Soybean Rust ◽  
Yield Potential ◽  
Stem Rot ◽  
Yield Losses ◽  
Sclerotinia Stem Rot ◽  
Soybean Yield ◽  
The Impact

Research must focus on management of diseases that cause extensive losses, especially when funds for research are limited. Knowledge of the losses caused by various soybean diseases is essential when prioritizing research budgets. The objective of this project was to compile estimates of soybean yield potential losses caused by diseases for each soybean producing state in the United States from 2006 to 2009. This data is of special interest since the 4-year period summarized in this report, permits an examination of the impact of soybean rust that was first reported in the United States in 2004. Thus, in addition to the goal of providing this information to aid funding agencies and scientists in prioritizing research objectives and budgets, an examination of the impact of soybean rust on soybean yield losses relative to other diseases is warranted. Yield losses caused by individual diseases varied among states and years. Soybean cyst nematode caused more yield losses than any other disease during 2006 to 2009. Seedling diseases, Phytophthora root and stem rot, sudden death syndrome, Sclerotinia stem rot, and charcoal rot ranked in the top six of diseases that caused yield loss during these years. Soybean yield losses due to soybean rust and Sclerotinia stem rot varied greatly over years, especially when compared to other diseases. Accepted for publication 21 October 2010. Published 22 November 2010.

scholarly journals Spatial and Multivariate Analysis of Soybean Yield in the State of Paraná-Brazil

2020 ◽  
Vol 8 (1) ◽  
pp. 387
Author(s):  
Denise Maria Grzegozewski ◽  
Elizabeth Giron Cima ◽  
Miguel Angel Uribe-Opazo ◽  
Luciana Pagliosa Carvalho Guedes ◽  
Jerry Adriani Johann
Keyword(s):  
Vegetation Indices ◽  
Spatial Association ◽  
Grain Filling ◽  
The State ◽  
Yield Potential ◽  
High Yield ◽  
P Value ◽  
Soybean Yield ◽  
Bivariate Correlation ◽  
Critical Phases

In this work, the aim was to evaluate the existence of spatial association of the municipal average official soybean yield (t ha-1) with agrometeorological data and vegetation indices. The information was observed by ten-day periods, in crop years 2010/2011, 2011/2012 and 2012/2013 in the State of Paraná. Local univariate spatial correlation (LISA index), as well as global bivariate correlation (L statistics) were calculated. With this study, we identified neighboring municipalities with high yield in the West as well as municipalities that are located with low-low yield Northwestern, showing positive spatial autocorrelation (IMG=1), significative (p-value < 0.05). In addition, there were differences between seeding times in different regions, and climate irregularity during flowering periods and grain filling in crop year 2011/2012 throughout the state, which caused a large drop in production in all municipalities of the state of Paraná. The analysis of local spatial association showed that in the three crop years, the Northwest region presented a significant low yield potential of soybean (p-value < 0.05). In addition, it was observed that the period from the 3rd ten-day period of October to the 2nd ten-day period of January was essential for the soybean cycle in the different regions of the state, since this period encompasses the critical phases of crop. Differences were also observed between the crop years studied, regarding the agrometeorological variables, which affected soybean yield mainly in the Western region of Paraná – Brazil.

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

Plant Disease ◽  
2016 ◽  
Vol 100 (10) ◽  
pp. 2152-2157 ◽  
Author(s):  
David A. Marburger ◽  
Damon L. Smith ◽  
Shawn P. Conley
Keyword(s):  
Sudden Death ◽  
Yield Loss ◽  
Maximum Yield ◽  
Planting Date ◽  
Sudden Death Syndrome ◽  
Yield Potential ◽  
Yield Losses ◽  
Planting Dates ◽  
Soybean Yield ◽  
The Impact

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

The Influence of Soil Water Content on Common Cocklebur (Xanthium strumarium) Interference in Soybeans (Glycine max)

Weed Science ◽  
1989 ◽  
Vol 37 (1) ◽  
pp. 76-83 ◽  
Author(s):  
David A. Mortensen ◽  
Harold D. Coble
Keyword(s):  
Soil Moisture ◽  
Moisture Stress ◽  
Field Studies ◽  
Yield Potential ◽  
Xanthium Strumarium ◽  
Growth Responses ◽  
Node Number ◽  
Soybean Yield ◽  
Canopy Development ◽  
Moisture Conditions

Field studies were conducted in 1985 and 1986 to evaluate the stability of reciprocal interference relationships between common cocklebur and soybean under high and low soil moisture conditions. A significant soil moisture differential was established with portable rain exclusion shelters. Well-watered and drought-stressed common cocklebur reduced soybean yield 29 and 12%, respectively. Drought-stressed common cocklebur interfered with soybean over a shorter distance and the magnitude of the effect at a given distance was reduced. The reduced common cocklebur interference in drier soils was attributed to both common cocklebur and soybean growth responses to moisture stress. First, moisture stress caused greater reductions in common cocklebur canopy diameter, stem diameter, node number, and plant height than in soybean. Second, the soybean yield potential was reduced by moisture stress. The reduction in yield potential decreased the effect of the weed interference. Third, soybean canopy development was slowed, and canopy closure that occurred in about 12 weeks in well-watered soybeans never occurred in the moisture-stressed soybeans. This reduced the degree of light interference between both the common cocklebur and soybean and among the soybean plants. The results of this study indicate that the reciprocal interference relationships between common cocklebur and soybean are not stable across soil moisture conditions. The implications of unstable competitive parameters must be considered as threshold models are developed for various field crops.

scholarly journals Economic Impact of Fluopyram-Amended Seed Treatments to Reduce Soybean Yield Loss Associated with Sudden Death Syndrome

Plant Disease ◽  
2021 ◽  
Vol 105 (1) ◽  
pp. 78-86 ◽  
Author(s):  
Amy M. Baetsen-Young ◽  
Scott M. Swinton ◽  
Martin I. Chilvers
Keyword(s):  
At Risk ◽  
Sudden Death ◽  
Seed Treatment ◽  
Yield Loss ◽  
Sudden Death Syndrome ◽  
Yield Potential ◽  
Worst Case ◽  
Soybean Yield ◽  
Treatment Research ◽  
Net Benefits

Soybean (Glycine max) sudden death syndrome (SDS), caused by Fusarium virguliforme, is a key limitation in reaching soybean yield potential, stemming from incomplete disease management through cultural practices and partial host resistance. A fungicidal seed treatment was released in 2014 with the active ingredient fluopyram and was the first chemical management strategy to reduce soybean yield loss stemming from SDS. Although farm level studies have found fluopyram profitable, we were curious to discover whether fluopyram would be beneficial nationally if targeted to soybean fields at risk for SDS yield loss. To estimate economic benefits of fluopyram adoption in SDS at-risk acres, in the light of U.S. public research and outreach from a privately developed product, we applied an economic surplus approach, calculating ex ante net benefits from 2018 to 2032. Through this framework of logistic adoption of fluopyram for alleviation of SDS-associated yield losses, we projected a net benefit of $5.8 billion over 15 years, considering the costs of public seed treatment research and future extension communication. Although the sensitivity analysis indicates that overall net benefits from fluopyram adoption on SDS at-risk acres are highly dependent upon the market price of soybean, the incidence of SDS, the adoption path, and ceiling of this seed treatment, the net benefits still exceeded $407 million in the worst-case scenario.

scholarly journals YieldNet: A Convolutional Neural Network for Simultaneous Corn and Soybean Yield Prediction Based on Remote Sensing Data

2020 ◽  
Author(s):  
Saeed Khaki ◽  
Hieu Pham ◽  
Lizhi Wang
Keyword(s):  
Remote Sensing ◽  
Continuous Monitoring ◽  
Large Scale ◽  
State Of The Art ◽  
Remote Sensing Data ◽  
Yield Potential ◽  
Soybean Yield ◽  
Growth State ◽  
Sensing Data ◽  
Learning Framework

AbstractLarge scale crop yield estimation is, in part, made possible due to the availability of remote sensing data allowing for the continuous monitoring of crops throughout its growth state. Having this information allows stakeholders the ability to make real-time decisions to maximize yield potential. Although various models exist that predict yield from remote sensing data, there currently does not exist an approach that can estimate yield for multiple crops simultaneously, and thus leads to more accurate predictions. A model that predicts yield of multiple crops and concurrently considers the interaction between multiple crop’s yield. We propose a new model called YieldNet which utilizes a novel deep learning framework that uses transfer learning between corn and soybean yield predictions by sharing the weights of the backbone feature extractor. Additionally, to consider the multi-target response variable, we propose a new loss function. Numerical results demonstrate that our proposed method accurately predicts yield from one to four months before the harvest, and is competitive to other state-of-the-art approaches.

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