Predicting eucalyptus plantation growth and yield using Landsat imagery in Minas Gerais, Brazil

Santos, Jeangelis Silva; Mendonça, Adriano Ribeiro de; Gonçalves, F. G.; Silva, Gilson Fernandes da; Almeida, André Quintão de; Silva, Jéferson Pereira Martins; Carvalho, Renata; Silva, Evandro Ferreira da; Aguiar, Marcelo

doi:10.1016/j.ecoinf.2023.102120

Cited by 7 publications

(2 citation statements)

References 52 publications

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“…A study by Santos et al (2023) aimed to propose a methodology utilizing Lautilizinga for predicting and projecting eucalyptus forest growth and yield and analyzing dynamics. Different scenarios with varying sample sizes were assessed to evaluate potential cost savings.…”

Section: Introductionmentioning

confidence: 99%

Quantifying effects of climate change and farmers' information demand on wheat yield in India: a deep learning approach with regional clustering

Godara,

Birthal,

Avinash

et al. 2024

Front. Sustain. Food Syst.

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IntroductionWith increasing demand for food and changing environmental conditions, a better understanding of the factors impacting wheat yield is essential for ensuring food security and sustainable agriculture. By analyzing the effect of multiple factors on wheat yield, the presented research provides novel insights into the potential impacts of climate change on wheat production in India. In the present study, datasets consisting of countrywide environmental and agronomic factors and wheat yield were collected. In addition, the study also analyzes the effect of information demand of farmers on production.MethodologyThe study employs a regional analysis approach by dividing the country into five zonal clusters: Northern Hills, Central India, Indo-Gangetic Plains, North-Eastern India, and Peninsular India. Correlation and Principal Component Analysis (PCA) were performed to uncover the month-wise key factors affecting wheat yield in each zone. Furthermore, four Machine Learning/Deep Learning-based models, including XGBoost, Multi-layer Perceptron (MLP), Gated Recurrent Unit (GRU), and 1-D Convolutional Neural Network (CNN), were developed to estimate wheat yield. This study estimated partial derivatives for all factors using Newton's Quotient Technique, a numerical method-based approach.ResultsThe analysis focused on applying this technique to the best-performing wheat yield estimation model, which was the GRU-based model (with RMSE and MAE of 0.60 t/ha and 0.46 t/ha, respectively).DiscussionIn the later sections of the article, multiple policy recommendations are communicated based on the extracted insights. The results of the presented research help inform decision-making regarding the development of strategies and policies to mitigate the impacts of climate change on wheat production in India.

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Section: Introductionmentioning

confidence: 99%

Quantifying effects of climate change and farmers' information demand on wheat yield in India: a deep learning approach with regional clustering

Godara,

Birthal,

Avinash

et al. 2024

Front. Sustain. Food Syst.

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show abstract

“…Agriculture has made great use of these approaches [24], and in this paper, we explore how forestry can benefit as well. Numerous recent studies have employed various machinelearning techniques to assess the productivity of planted forests [25][26][27][28][29]. However, a notable limitation across these studies is the lack of comprehensive exploration regarding the tools utilized, deeper data visualizations, and the production of applicable and reproducible results and conclusions for end users.…”

Section: Introductionmentioning

confidence: 99%

Decision-Tree Application to Predict and Spatialize the Wood Productivity Probabilities of Eucalyptus Plantations

Álvares

Cegatta

Scolforo

et al. 2023

Forests

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Brazil is one of the world’s wood short-fiber producers, cultivating 7.5 million hectares of eucalypt trees. Foresters and resource managers often face difficulties in surveying reliable Eucalyptus productivity levels for the purpose of purchasing and prospecting lands. Spatial data science (DS) and machine learning (ML) provide powerful approaches to make the best use of the large datasets available today. Agriculture has made great use of these approaches, and in this paper, we explore how forestry can benefit as well. We hypothesized that both DS and ML techniques can be used to improve Eucalyptus productivity zoning based on multiple operational datasets of tree growth and environment. Based on more than 12,000 permanent forest inventory plots of commercial Eucalyptus plantations and the climate, soil, and altitude variables associated with them, a supervised ML approach was adjusted to model the forest plantation productivity. A multi-tuning of the decision-tree (DT) algorithm hyperparameters was prepared to yield 450 DT models, with a better one delivering an RMSE of 53.5 m3 ha−1, split in 35 terminal nodes, here interpreted as Eucalyptus productivity zones. The DT model showed an optimum performance index of 0.83, a coefficient of determination of 0.91, a root mean squared error of 12.3 m3 ha−1, and a mean absolute percentage error only of 3.1% in predicting the testing dataset throughout the study area. The DT rule set was interpreted in a user-friendly table and was prepared to classify any location within the study area in each one of the 35 productivity zones based on the required environment variables of the DT algorithm. The high quality of the model obtained made it possible to spatialize the DT rules, providing a reliable cartographic visualization of the probability levels of true Eucalyptus productivity for a huge region of forest-based industries in Brazil. These data-science techniques also provided a yield gap analysis using a very down-to-earth approach. We estimated a yield gap by an amount of 4.2 × 107 m3, representing a few more than 113,000 ha, or 15% of the current forest base. This is the amount of avoided area expansion to accumulate the same wood stock in case the productivity is raised to the attainable level in each zone. This present study provided deeper analysis and reproducible tools to manage forest assets sustainably.

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Construction of multidimensional features to identify tea plantations using multisource remote sensing data: A case study of Hangzhou city, China

Kang

Chen

et al. 2023

Ecological Informatics

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Predicting eucalyptus plantation growth and yield using Landsat imagery in Minas Gerais, Brazil

Cited by 7 publications

References 52 publications

Quantifying effects of climate change and farmers' information demand on wheat yield in India: a deep learning approach with regional clustering

Quantifying effects of climate change and farmers' information demand on wheat yield in India: a deep learning approach with regional clustering

Decision-Tree Application to Predict and Spatialize the Wood Productivity Probabilities of Eucalyptus Plantations

Construction of multidimensional features to identify tea plantations using multisource remote sensing data: A case study of Hangzhou city, China

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