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30 results for “soybean yield”
Soybean yield projections in Europe under historical (1981-2010) and future climate (2050-2059 and 2090-2099 for RCP4.5 and RCP8.5)
<p><strong>General information</strong></p> <p>This dataset contains soybean yield projections in Europe under historical (1981-2010) and future climate with moderate (RCP 4.5) to intense (RCP 8.5) warming, up to the 2050s and 2090s time horizons. The data has been generated by <em>Guilpart et al. (2022) Data-driven projections suggest large opportunities to improve Europe's soybean self-sufficiency under climate change, Nature Food. </em>All details can be found in this paper. A brief summary is provided below.</p> <p><strong>Summary of soybean yield projections methodology</strong></p> <p>Yield projections have been performed using data-driven relationships between climate and soybean yield derived from machine-learning (Random Forest). The Random Forest model was trained using (i) the the global dataset of historical yields updated version (Iizumi et al. 2014a), which includes grid-wise soybean yields worldwide with the grid size of 1.125 degree over 1981-2010, and (ii) the global retrospective meteorological forcing dataset tailored for agricultural application (GRASP, Iizumi et al. 2014b), which covers the period 1961–2010 at the same spatial resolution as yield data, i.e. a grid size of 1.125 degree. Time-detrended soybean yield data was related (using Random Forest) to 35 climate variables defined at a monthly time step over the seven months of the soybean growing season, plus the fraction of irrigated area, i.e. a total of 36 variables. The 35 climate variables are monthly mean daily minimum and maximum temperatures (<em>Tmin</em> and <em>Tmax</em>, degree Celsius), monthly total precipitation (<em>rain</em>, mm month<sup>-1</sup>), monthly mean daily total solar radiation (<em>solar</em>, MJ m<sup>-2</sup> day<sup>-1</sup>), monthly mean air vapor pressure (VP, hPa). The fitted model showed high R² (higher than 0.9) and low RMSE (0.35 t ha<sup>-1</sup>) between observed and predicted yields based on cross-validation.</p> <p>Then, soybean yield projections under historical over whole Europe have been performed using the GRASP climate data, and yield projections under future climate have been performed using 16 climate change scenarios consisting of bias-corrected data of eight Global Circulation Models (GCM; GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC5, MIROC-ESM, MIROC-ESM-CHEM, MRI-CGCM3, and NorESM1-M, used in the Coupled Model Intercomparison phase 5 (CMIP5) and two Representative Concentration Pathways (RCPs; 4.5 and 8.5 W m<sup>-2</sup>). Soybean growing season used for projections is April to October. All projections assumed irrigated fraction equals to zero. Projections are shown only on agricultural area (cropland plus pasture), in the year 2000. Soybean yield is expressed in tons per hectare.</p> <p><strong>Files description</strong></p> <ul> <li><em>RF_soybean_historical_GRASP_median_1981_2010.nc</em> : random forest projections of soybean yield in Europe for the historical (1981-2010) period using GRASP climate data. This file contains the median yield (in tons per hectare) over 1981-2010.</li> <li><em>RF_soybean_rcp45_median_2050_2059.nc : </em>random forest projections of soybean yield in Europe for the 2050-2059 time period under RCP4.5. This file contains the median yield (in tons per hectare) over 2050-2059 and the 8 GCMs.</li> <li><em>RF_soybean_rcp45_median_2090_2099.nc : </em>random forest projections of soybean yield in Europe for the 2090-2099 time period under RCP4.5. This file contains the median yield (in tons per hectare) over 2090-2099 and the 8 GCMs.</li> <li><em>RF_soybean_rcp85_median_2050_2059.nc : </em>random forest projections of soybean yield in Europe for the 2050-2059 time period under RCP8.5. This file contains the median yield (in tons per hectare) over 2050-2059 and the 8 GCMs.</li> <li><em>RF_soybean_rcp85_median_2090_2099.nc : </em>random forest projections of soybean yield in Europe for the 2090-2099 time period under RCP8.5. This file contains the median yield (in tons per hectare) over 2090-2099 and the 8 GCMs.</li> </ul> <p><strong>References</strong></p> <p>Guilpart N. <em>et al.</em> (2022)<strong> </strong>Data-driven projections suggest large opportunities to improve Europe's soybean self-sufficiency under climate change, <em>Nature Food</em>.</p> <p>Iizumi T. <em>et al.</em> (2014a) Historical changes in global yields: Major cereal and legume crops from 1982 to 2006. <em>Glob. Ecol. Biogeogr.</em> 23, 346–357.</p> <p>Iizumi T. <em>et al</em>. (2014b). A meteorological forcing data set for global crop modeling: Development, evaluation, and intercomparison. <em>J. Geophys. Res. Atmos. Res.</em> 119, 363–384.</p>
NortheastChinaSoybeanYield20m: an annual soybean yield dataset at 20 m in Northeast China from 2019 to 2023
<p>Accurate monitoring of crop yield is important for ensuring food security. Current yield estimation methods, such as machine learning models or the assimilation of remotely sensed biophysical variables into crop growth models, depend heavily on ground observations and involve significant computational costs. To solve these problems, a hybrid framework coupling the World Food Studies Simulation Model (WOFOST) and the Gated Recurrent Unit model (GRU) was proposed for soybean yield estimation in Northeast China from 2019 to 2023.</p> <p>This dataset provides 20 m annual soybena yield in Northeast China from 2019 to 2023.</p> <p>*** The data file is in “.tif" format</p> <p>*** Temporal Resolution: annually</p> <p>*** Temporal coverage: 2019-2023</p> <p>*** Pixel size: 20 m</p> <p>*** Projection information: EPSG: 4326</p>
Figure 4 in Effects of Palmer Amaranth (Amoronthus polmeri) Establishment Time and Distance from the Crop Row on Biological and Phenological Characteristics of the Weed: Implications on Soybean Yield
Figure 4. Effects of the interaction of weed establishment time and distance from the crop row on Amoronthus polmeri dry weight before soybean harvest. Vertical bars represent ± standard error of the mean (SE2014 = 1.27; SE2015 = 0.74) from the analysis for comparisons between weed establishment times with sample size n = 72. WAE, weeks after soybean emergence.
Figure 3 in Effects of Palmer Amaranth (Amoronthus polmeri) Establishment Time and Distance from the Crop Row on Biological and Phenological Characteristics of the Weed: Implications on Soybean Yield
Figure 3. Effects of the interaction of weed establishment time and distance from the crop on Amoronthus polmeri plant height at harvest. Vertical bars represent ± standard error of the mean (SE2014 = 4.68; SE2015 = 3.14) from the analysis for comparisons between weed establishment times with sample size n = 72. WAE, weeks after soybean emergence.
Figure 7 in Effects of Palmer Amaranth (Amoronthus polmeri) Establishment Time and Distance from the Crop Row on Biological and Phenological Characteristics of the Weed: Implications on Soybean Yield
Figure 7. Relationship between ground cover and extinction coefficient for each sampling date (n = 12 plots) throughout the 2014 growing season. WAE, weeks after soybean emergence.
Figure 2 in Effects of Palmer Amaranth (Amoronthus polmeri) Establishment Time and Distance from the Crop Row on Biological and Phenological Characteristics of the Weed: Implications on Soybean Yield
Figure 2. Soybean and Amoronthus polmeri (AMAPA) height (averaged across distance from the crop) at 0, 1, 2, 4, 6, and 8 wk after soybean emergence (WAE) (i.e., AMAPA-0, AMAPA-1, AMAPA-2, AMAPA-4, AMAPA-6, and AMAPA-8, respectively). Vertical bars represent ± standard error of the mean from the analysis for comparisons within each sampling date (i.e., n = 12 for 0 WAE, 24 for 1 WAE, etc.).
Figure 9 in Effects of Palmer Amaranth (Amoronthus polmeri) Establishment Time and Distance from the Crop Row on Biological and Phenological Characteristics of the Weed: Implications on Soybean Yield
Figure 9. Effects of weed establishment time on soybean yield averaged across Amoronthus polmeri distances from the crop row. Dashed lines indicate the confidence intervals at 95% confidence level (sample size n = 72). WAE, weeks after soybean emergence.
Figure 10 in Effects of Palmer Amaranth (Amoronthus polmeri) Establishment Time and Distance from the Crop Row on Biological and Phenological Characteristics of the Weed: Implications on Soybean Yield
Figure 10. Effects of Amoronthus polmeri distance from the soybean row on crop yield averaged across A. polmeri establishment times. Vertical bars represent ± standard error of the mean (SE2014 = 337.45; SE2015 = 207.14) from the analysis for comparisons between A. polmeri distances from the crop with sample size n = 72.
Figure 6 in Effects of Palmer Amaranth (Amoronthus polmeri) Establishment Time and Distance from the Crop Row on Biological and Phenological Characteristics of the Weed: Implications on Soybean Yield
Figure 6. Effects of weed establishment time on Amoronthus polmeri (AMAPA) flowering (averaged across distance from the crop) at various sampling occasions for 0, 1, 2, 4, 6, and 8 wk after soybean emergence (WAE) (i.e., AMAPA-0, AMAPA-1, AMAPA-2, AMAPA-4, AMAPA-6, and AMAPA-8 respectively) in 2014 and 2015. Vertical bars represent ± standard error of the mean (i.e., flowering of the entire A. polmeri population was evaluated at each sampling occasion) from the analysis for comparisons within each sampling date (i.e., n = 12 plots for 0 WAE, 24 plots for 1 WAE, 36 plots for 2 WAE, etc.).
Figure 5 in Effects of Palmer Amaranth (Amoronthus polmeri) Establishment Time and Distance from the Crop Row on Biological and Phenological Characteristics of the Weed: Implications on Soybean Yield
Figure 5. Effects of the interaction of weed establishment time and distance from the crop row on Amoronthus polmeri seed production before soybean harvest. Vertical bars represent ± standard error of the mean (SE2014 = 2,530.27; SE2015 = 1,008.30) from the analysis for comparisons between weed establishment times with sample size n = 72. WAE, weeks after soybean emergence.
Figure 8 in Effects of Palmer Amaranth (Amoronthus polmeri) Establishment Time and Distance from the Crop Row on Biological and Phenological Characteristics of the Weed: Implications on Soybean Yield
Figure 8. Relationship between ground cover and extinction coefficient for each sampling date (n = 12 plots) throughout the 2015 growing season. WAE, weeks after soybean emergence
Figure 1 in Effects of Palmer Amaranth (Amoronthus polmeri) Establishment Time and Distance from the Crop Row on Biological and Phenological Characteristics of the Weed: Implications on Soybean Yield
Figure 1. Schematic representation of the experimental setup depicting the distance of Amoronthus polmeri (AMAPA) from the crop (i.e., 0, 24, and 48 cm from the soybean row) and the sequence of A. polmeri establishment time (i.e., 0, 1, 2, 4, 6, and 8 wk after soybean emergence [WAE] or AMAPA-0, AMAPA-1, AMAPA-2, AMAPA-4, AMAPA-6, and AMAPA-8, respectively). Each treatment combination (i.e., establishment time × distance from the crop) was applied only to one randomly selected experimental plot per replication.
Figure 3 in Does narrow row spacing suppress weeds and increase yields in corn and soybean? A meta-analysis
Figure 3. The overall effect of narrow row spacing (<76 cm) on weed density, weed biomass,weed control,weed seed production,and crop yield.The vertical black dashed line indicates zero effect. The black dots represent mean effect sizes (log of response ratios [lnðRRÞ]), and the black lines represent their respective 95% confidence intervals (CIs). The numbers in parentheses indicate the number of observations followed by the number of studies for each effect size. The effect sizes were considered significantly different when their 95% CIs did not overlap or contain zero.
Figure 6 in Does narrow row spacing suppress weeds and increase yields in corn and soybean? A meta-analysis
Figure 6. The effect of narrow row spacing (<76 cm) on crop yield as explained by subgroups of the crop, tillage, weed type, weed management method, herbicide application frequency, and time. The vertical black dashed line indicates zero effect. The black dots represent mean effect sizes (log of response ratios [lnðRRÞ]) for each subgroup, and the black lines represent their respective 99% confidence intervals (CIs). The numbers in parentheses indicate the number of observations followed by the number of studies for each effect size. The effect sizes were considered significantly different when their 99% CIs did not overlap or contain zero.
Figure 2. A in Does narrow row spacing suppress weeds and increase yields in corn and soybean? A meta-analysis
Figure 2. A map of the states in the midwestern and eastern United States showing experimental sites for the 35 corn and soybean narrow row spacing studies included in the meta-analysis.
Figure 5 in Does narrow row spacing suppress weeds and increase yields in corn and soybean? A meta-analysis
Figure 5. The individual effect sizes (natural log of response ratios [lnðRRÞ]) of (A) weed density, (B) weed biomass,(C) weed control, (D) weed seed production, and (E) crop yield as a function of crop row spacing. The green and red dots represent individual effect sizes for corn and soybean, respectively. The horizontal black dashed line represents zero effect,while the vertical black line represents 76-cm row spacing (control).The black bold line shows the relationship between individual effect sizes and crop row spacing,which is given as R (Pearson's correlation) with a P-value. The gray-shaded area represents 95% confidence intervals (CIs) of the linear relationship.
Figure 8 in Does narrow row spacing suppress weeds and increase yields in corn and soybean? A meta-analysis
Figure 8. Sensitivity analysis showing the variation in overall effect sizes (log of response ratios [ln(RR)]) (mean ± 95% confidence intervals [CIs]) of narrow row spacing effects on (A) weed density, (B) weed biomass, (C) weed control, (D) weed seed production, and (E) crop yield when any specific study was excluded from the analysis. The vertical red solid and dashed lines represent the mean ± 95% CIs, respectively, of overall effect sizes with all the studies included in the analysis.
Figure 1 in Does narrow row spacing suppress weeds and increase yields in corn and soybean? A meta-analysis
Figure 1. PRISMA (Preferred Reporting Items for Systematic Reviews and MetaAnalyses; Page et al. 2021) flow diagram showing the stepwise procedure used for selecting 35 studies for meta-analysis.
Figure 4 in Does narrow row spacing suppress weeds and increase yields in corn and soybean? A meta-analysis
Figure 4. The effect of narrow row spacing (<76 cm) on (A) weed density, (B) weed biomass, (C) weed control, and (D) weed seed production as explained by the subgroups of crop,tillage,weed type,weed management method, herbicide application frequency, and time. The vertical black dashed line indicates zero effect.The black dots represent mean effect sizes (log of response ratios [lnðRRÞ]) for each subgroup, and the black lines represent their respective 99% confidence intervals (CIs). The numbers in parentheses indicate the number of observations followed by the number of studies for each effect size. The effect sizes were considered significantly different when their 99% CIs did not overlap or contain zero.
Figure 7 in Does narrow row spacing suppress weeds and increase yields in corn and soybean? A meta-analysis
Figure 7. Density plots show the distribution of individual effect sizes (log of response ratios [ln(RR)]) of weed density,biomass,control, weed seed production, and crop yield.
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