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 4 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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forestGYM: Forest Growth and Yield Model Based on Clutter Model

Chai

2024

CRAN: Contributed Packages

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Type Package Title Forest Growth and Yield Model Based on Clutter Model Version 1.0.0 Depends R (>= 3.5.0) Imports gtools(>= 3.8.5),stats(>= 4.3.1) Description The Clutter model is a significant forest growth simulation tool. Grounded on individual trees and comprehensively considering factors such as competition among trees and the impact of environmental elements on growth, it can accurately reflect the growth process of forest stands. It can be applied in areas like forest resource management, harvesting planning, and ecological research. With the help of the Clutter model, people can better understand the dynamic changes of forests and provide a scientific basis for rational forest management and protecting the ecological environment. This R package can effectively realize the construction of forest growth and harvest models based on the Clutter model and achieve optimized forest management.References:

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

Cited by 4 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

forestGYM: Forest Growth and Yield Model Based on Clutter Model

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