Alternatives to Growth and Yield Prognosis for Pinus caribaea var. caribaea Barrett &amp; Golfari

Guera, Ouorou Ganni Mariel; Silva, José Antônio Aleixo da; Ferreira, Rinaldo Luíz Caraciolo; Lazo, Daniel Alberto Álvarez; Medel, Héctor Barrero

doi:10.1590/2179-8087.038117

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…Mathematical modeling refers to the development or adjustment of mathematical models that describe the behavior of a variable of interest (Guera et al, 2019). Regression analysis is the primary technique for formulating empirical mathematical models.…”

Section: Multiple Linear Regression Model With Dummy Variables (Mlrmd...mentioning

confidence: 99%

Dummy Variable Regression and Artificial Neural Networks for Maize Grain Yields Prediction in Push-Pull Systems

Guera¹,

Castrejón-Ayala

Robledo

et al. 2023

Preprint

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Crop yield prediction is essential for sustainable production planning. In agroecological systems, the traditional linear or non-linear regression models used for this purpose present limitations and robustness problems due to the number of variables generated by the complexity of these systems. Therefore, the present study was carried out with the objectives of 1) fitting multiple linear regression models with dummy variables using the Ordinary Least Squares method (OLS); 2) parameterizing and training Artificial Neural Networks (ANNs) with the backpropagation algorithm; and 3) comparing the performance of both approaches in maize yield prediction in push-pull systems established in Yautepec, Morelos, Mexico. In both modeling approaches, maize grain yield predictive variables were: edaphoclimatic (soil temperature and moisture), phytosanitary (incidence and severity of Spodoptera frugiperda), morphological (leaf area index), and categorical (Blocks, Management Systems) variables. The ANN of architecture MLP 18-13-1 (r = 0.95; RMSE = 12.19%), with hyperbolic tangent activation function in the hidden layer and linear function in the output layer, generated consistent and more accurate predictions than those obtained with the regression equation with dummy variables (r = 0.87; R2 = 0.75; RMSE = 20.38%).

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Section: Multiple Linear Regression Model With Dummy Variables (Mlrmd...mentioning

confidence: 99%

Dummy Variable Regression and Artificial Neural Networks for Maize Grain Yields Prediction in Push-Pull Systems

Guera¹,

Castrejón-Ayala

Robledo

et al. 2023

Preprint

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Quantile regression: prediction of growth and yield for a eucalyptus plantation in northeast Brazil

et al. 2021

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Assessment of a Monthly Data Structure for Growth and Yield Projections from Early to Harvest Age in Hybrid Eucalypt Stands

Casas,

Boechat Soares,

Romarco de Oliveira

et al. 2023

JTAS

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Whole-stand Models (WSM) have always been fitted with permanent plot data organised in a sequential age-matched database, i.e., i and i+1, where i = 1, 2, ... N plot measurements. The objectives of this study were (1) to evaluate the statistical efficiency of a monthly distributed data structure by fitting the models of Clutter (1963), Buckman (1962) in the version modified by A. L. da Silva et al. (2006), and deep learning, and (2) to evaluate the possibility of gaining accuracy in yield projections made from an early age to harvest age of eucalypt stands. Three alternatives for organizing the data were analyzed. The first is with data paired in sequential measurement ages, i.e., i and i+1, where i = 1, 2, ... N plot measurements. In the second, all possible measurement intervals for each plot were considered, i.e., ii+1; i, i+2; ...; iN; i+1, i+2; ..., N-1, N. The third has data paired by month (j), always with an interval of one month, i.e., j, j+1; j+1, j+2; j+M-1, M, where M is the stand age of the plot measurement in months. This study shows that the accuracy and consistency of the projections depend on the organization of the monthly distributed data, except for the Clutter model. A better alternative to increasing the statistical assumptions of the forecast from early to harvest age is based on a monthly distributed data structure using a deep learning method.

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Alternatives to Growth and Yield Prognosis for Pinus caribaea var. caribaea Barrett & Golfari

Cited by 3 publications

References 15 publications

Dummy Variable Regression and Artificial Neural Networks for Maize Grain Yields Prediction in Push-Pull Systems

Dummy Variable Regression and Artificial Neural Networks for Maize Grain Yields Prediction in Push-Pull Systems

Quantile regression: prediction of growth and yield for a eucalyptus plantation in northeast Brazil

Assessment of a Monthly Data Structure for Growth and Yield Projections from Early to Harvest Age in Hybrid Eucalypt Stands

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