Soybean Yield Estimation and Its Components: A Linear Regression Approach

Wei, Marcelo Chan Fu; Molin, José Paulo

doi:10.3390/agriculture10080348

Cited by 29 publications

(18 citation statements)

References 82 publications

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“…R 2 = 0.67) model. These results are similar to linear multiple regression for soybean yield prediction using yield components (R 2 = 0.70) [43]. However, the Lasso variable selection provides a more practical use as only NDVI and FGCC measurements are necessary with a relatively negligible Adj.…”

Section: Resultssupporting

confidence: 75%

Using Canopy Measurements to Predict Soybean Seed Yield

Schmitz

Kandel

2021

Remote Sensing

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Predicting soybean [Glycine max (L.) Merr.] seed yield is of interest for crop producers to make important agronomic and economic decisions. Evaluating the soybean canopy across a range of common agronomic practices, using canopy measurements, provides a large inference for soybean producers. The individual and synergistic relationships between fractional green canopy cover (FGCC), photosynthetically active radiation (PAR) interception, and a normalized difference vegetative index (NDVI) measurements taken throughout the growing season to predict soybean seed yield in North Dakota, USA, were investigated in 12 environments. Canopy measurements were evaluated across early and late planting dates, 407,000 and 457,000 seeds ha−1 seeding rates, 0.5 and 0.8 relative maturities, and 30.5 and 61 cm row spacings. The single best yield predictor was an NDVI measurement at R5 (beginning of seed development) with a coefficient of determination of 0.65 followed by an FGCC measurement at R5 (R2 = 0.52). Stepwise and Lasso multiple regression methods were used to select the best prediction models using the canopy measurements explaining 69% and 67% of the variation in yield, respectively. Including plant density, which can be easily measured by a producer, with an individual canopy measurement did not improve the explanation in yield. Using FGCC to estimate yield across the growing season explained a range of 49% to 56% of yield variation, and a single FGCC measurement at R5 (R2 = 0.52) being the most efficient and practical method for a soybean producer to estimate yield.

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Section: Resultssupporting

confidence: 75%

Using Canopy Measurements to Predict Soybean Seed Yield

Schmitz

Kandel

2021

Remote Sensing

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“…According to Wei and Molin (2020), the linear regression models, based on the 1 000-seed weight and the number of seeds per soybean pod, demonstrated low (r = 0.50) and high (r = 0.92), respectively, linear correlations with the seed yield. In the present research, the values were considerably lower, especially for the 1 000-seed weight (Table 5).…”

Section: Resultsmentioning

confidence: 99%

Effect of soybean cultivars sowing dates on seed yield and its correlation with yield parameters

Borowska¹,

Prusiński²

2021

Plant Soil Environ.

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The article presents the effect of three sowing dates on the growth, development and yielding of four soybean cultivars of different earliness and under different temperature and precipitation conditions across the years. The seed yield from early sowing significantly correlated with the total precipitation in June and July, and at later dates, also with the total precipitation in August. The significantly highest soybean yields were collected from the sowing at a turn of April and May, and the highest seed and protein yield, as well as protein content in seed, were recorded for the mid-early Merlin cultivar. Neither the number and the seed weight per pod nor the 1 000-seed weight significantly depended on the sowing date. Over years, a significant, almost linear decrease in the plant height and the first pod setting height, the weight of nodules, the protein yield and the LAI (leaf area index) value was observed. High significant correlations were found between the seed yield and the plant height and the first pod setting height, as well as between the seed number and the seed weight per pod and the 1 000-seed weight as well as between the plant height and the first pod setting height.

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“…Among all the tested yield component traits in this study, NP showed the second-highest linear correlation with total seed yield and showed a positive correlation with PP. Many studies reported that the variations in the number of nodes per plant is usually accounted for the changes in the number of pods per plant [ 81 – 84 ]. A negative correlation between the total soybean seed yield and NRNP was found in this study.…”

Section: Discussionmentioning

confidence: 99%

Application of machine learning and genetic optimization algorithms for modeling and optimizing soybean yield using its component traits

Yoosefzadeh-Najafabadi

Tulpan

Eskandari

2021

PLoS ONE

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Improving genetic yield potential in major food grade crops such as soybean (Glycine max L.) is the most sustainable way to address the growing global food demand and its security concerns. Yield is a complex trait and reliant on various related variables called yield components. In this study, the five most important yield component traits in soybean were measured using a panel of 250 genotypes grown in four environments. These traits were the number of nodes per plant (NP), number of non-reproductive nodes per plant (NRNP), number of reproductive nodes per plant (RNP), number of pods per plant (PP), and the ratio of number of pods to number of nodes per plant (P/N). These data were used for predicting the total soybean seed yield using the Multilayer Perceptron (MLP), Radial Basis Function (RBF), and Random Forest (RF), machine learning (ML) algorithms, individually and collectively through an ensemble method based on bagging strategy (E-B). The RBF algorithm with highest Coefficient of Determination (R2) value of 0.81 and the lowest Mean Absolute Errors (MAE) and Root Mean Square Error (RMSE) values of 148.61 kg.ha-1, and 185.31 kg.ha-1, respectively, was the most accurate algorithm and, therefore, selected as the metaClassifier for the E-B algorithm. Using the E-B algorithm, we were able to increase the prediction accuracy by improving the values of R2, MAE, and RMSE by 0.1, 0.24 kg.ha-1, and 0.96 kg.ha-1, respectively. Furthermore, for the first time in this study, we allied the E-B with the genetic algorithm (GA) to model the optimum values of yield components in an ideotype genotype in which the yield is maximized. The results revealed a better understanding of the relationships between soybean yield and its components, which can be used for selecting parental lines and designing promising crosses for developing cultivars with improved genetic yield potential.

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Soybean Yield Estimation and Its Components: A Linear Regression Approach

Cited by 29 publications

References 82 publications

Using Canopy Measurements to Predict Soybean Seed Yield

Using Canopy Measurements to Predict Soybean Seed Yield

Effect of soybean cultivars sowing dates on seed yield and its correlation with yield parameters

Application of machine learning and genetic optimization algorithms for modeling and optimizing soybean yield using its component traits

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