Sample size and modeling of plant variability using precision statistics in soybean counting traits

Souza, Renato Santos de; Toebe, Marcos; Marchioro, Volmir Sérgio; Filho, Alberto Cargnelutti; Bittencourt, Karina Chertok; Mello, Anderson Chuquel; Paraginski, João Antônio

doi:10.1016/j.fcr.2022.108789

Cited by 4 publications

(3 citation statements)

References 34 publications

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“…This may be explained by the variability between the plants measured within the experimental units, which was slightly higher in the experiments performed in the high-altitude location, specially E3. In the resampling with replacement, this effect is absorbed (Souza et al 2022(Souza et al , 2023a in the estimate of 𝛩 𝜀 , providing higher confidence limits (Schönbrodt and Perugini 2013;Anderson et al 2017) and, consequently, making it necessary to increase the number of plants sampled per experimental unit. Differently from the analyses performed individually for each experiment, the joint analysis expressed greater robustness since smaller sample sizes were enough to estimate CV1, CV2, CV3, and CV4.…”

Section: Discussionmentioning

confidence: 99%

A matter of genetic divergence: sizing up the sample for soybean canonical variables

de Souza,

Filho,

Toebe

et al. 2023

Preprint

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Empirical sampling can result in inaccurate estimates of the variance captured in canonical variables, therefore affecting their scores and the identification of genetic divergence. This study aimed to analyze the response of canonical variables as a function of the number of plants sampled per experimental unit, and to define a representative multivariate sample size based on the percentage variance absorbed by the canonical variables. Six soybean experiments were performed in two locations in Rio Grande do Sul, Brazil, using a complete randomized block experimental design with three repetitions and 20 genotypes (360 plots), and ten traits were assessed in 20 plants per plot. Bootstrap resampling was applied for the canonical variable analysis. Posteriorly, sample size per experimental unit was dimensioned using nonlinear models and defining the maximum curvature point via perpendicular distances. The estimate of the percentage variance retained in the canonical variables was sensitive to the sample size per experimental unit. The 95% confidence interval width of the absorbed variance decreased as sample size increased, and the precision for estimating the variance was improved, stabilizing once 36 plants per experimental unit were sampled. Insufficient sampling harms the identification of divergent genotypes, thus increasing sample size gradually improves the quality of the canonical variables’ variance estimates. Thirty-six plants per experimental unit are enough to estimate the variance explained in the first four canonical variables for soybean reliably. The sample size recommendations presented may be useful for researchers in the genetic divergence field, increasing the efficiency of soybean breeding programs.

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Section: Discussionmentioning

confidence: 99%

A matter of genetic divergence: sizing up the sample for soybean canonical variables

de Souza,

Filho,

Toebe

et al. 2023

Preprint

Self Cite

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“…When harvesting a specific crop, it holds the potential to impact or even disrupt neighboring crops, thereby exerting an influence on the final yield [32]. This research employed a block sampling approach to minimize field disturbance during crop field trials.…”

Section: Field Experimental Designmentioning

confidence: 99%

Optimal Planting Density and Nutrient Application of Soybeans: A Case Study in Northeastern China

Hao,

Lv,

Wang

2023

Agronomy

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In the context of the Chinese government’s policy guidance, there is black soil protection and ecological environment protection. The purpose of this paper is to solve the problem that the soil ecology of the black soil in Northeast China is changing year by year, and it is necessary to explore the sowing and fertilization strategy under the new situation; most Chinese growers rely excessively on their personal experience in the process of soybean sowing and fertilization. In this study, we used “Heihe 43” soybeans and used regression experimental design methods to analyze the effects of planting density, nitrogen, phosphorus, and potassium fertilizer application on soybean yield and to determine the optimal planting density and fertilizer ratios. The study reveals that the optimal soybean planting density in Northeast China is 45.37 × 104 plants/ha, with nitrogen at 98.4 kg/ha, phosphorus at 218.96 kg/ha, and potash at 47.62 kg/ha. Under these conditions, soybean yields can reach 3816.67 kg/ha. This study can provide a theoretical method for decision-making to obtain the optimal planting density and fertilizer ratio for different regions of the farming system.

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“…This information, while interesting, is labor-intensive and may not provide useful and accurate information when applied to the whole field scale. For example, de Souza et al (2023) reported that to assess a plant's phenotypic characteristic, four traits from 21 soybean plants contained within a 2.7-m 2 area should be assessed. However, when this sampling protocol, designed for small plots, is extended across fields that may be greater than 650,000 m 2 (65 ha), the sampling requirement quickly becomes unmanageable.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence and satellite‐based remote sensing can be used to predict soybean (Glycine max) yield

Joshi,

Clay,

Sharma

et al. 2023

Agronomy Journal

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Because the manual counting of soybean (Glycine max) plants, pods, and seeds/pods is unsuitable for soybean yield predictions, alternative methods are desired. Therefore, the objective was to determine if satellite remote sensing−based artificial intelligence (AI) models could be used to predict soybean yield. In the study, multiple remote sensing−based AI models were developed for soybean growth stage ranging from VE/VC (plant emergence) to R6/R7 (full seed to beginning maturity). The ability of the Deep Neural Network (DNN), Support Vector Machine (SVM), Random Forest (RF), Least Absolute Shrinkage and Selection Operator (LASSO), and AdaBoost to predict soybean yield, based on blue, green, red, and near infrared reflectance data collected by the PlanetScope satellite at 6 growth stages, was determined. Remote sensing and soybean yield monitor data from 3 different fields in two years (2019 and 2021) were aggregated into 24,282 grid cells that had the dimensions of 10 by 10m. A comparison across models showed that the DNN outperformed the other models. Moreover, as crops matured from VE/VC to R4/R5, the R2 value of the models increased from 0.26 to over 0.70. These findings indicate that remote sensing data collected at different growth stages can be combined for soybean yield predictions. Moreover, additional work needs to be conducted to assess the model's ability to predict soybean yield with vegetation indices (VI) data for fields not used to train the model.This article is protected by copyright. All rights reserved

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Sample size and modeling of plant variability using precision statistics in soybean counting traits

Cited by 4 publications

References 34 publications

A matter of genetic divergence: sizing up the sample for soybean canonical variables

A matter of genetic divergence: sizing up the sample for soybean canonical variables

Optimal Planting Density and Nutrient Application of Soybeans: A Case Study in Northeastern China

Artificial intelligence and satellite‐based remote sensing can be used to predict soybean (Glycine max) yield

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