Using boosted tree regression and artificial neural networks to forecast upland rice yield under climate change in Sahel

Zhang, Lei; Traoré, Seydou; Ge, Jun; Li, Yanbin; Wang, Shunsheng; Zhu, Ge; Cui, Yuanlai; Fipps, Guy

doi:10.1016/j.compag.2019.105031

Cited by 51 publications

(29 citation statements)

References 36 publications

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“…One important common conclusion was found from all above cited references; there is always an error threshold in the prediction as the prediction is based on the climate data. e same finding can be seen in many other related studies [43,44]. In addition to the changing climate, spatial distribution of climatic data and the quality of the climatic data can trigger the error.…”

Section: Resultssupporting

confidence: 79%

Artificial Neural Network to Estimate the Paddy Yield Prediction Using Climatic Data

Amaratunga

Wickramasinghe

Perera

et al. 2020

Mathematical Problems in Engineering

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Paddy harvest is extremely vulnerable to climate change and climate variations. It is a well-known fact that climate change has been accelerated over the past decades due to various human induced activities. In addition, demand for the food is increasing day-by-day due to the rapid growth of population. Therefore, understanding the relationships between climatic factors and paddy production has become crucial for the sustainability of the agriculture sector. However, these relationships are usually complex nonlinear relationships. Artificial Neural Networks (ANNs) are extensively used in obtaining these complex, nonlinear relationships. However, these relationships are not yet obtained in the context of Sri Lanka; a country where its staple food is rice. Therefore, this research presents an attempt in obtaining the relationships between the paddy yield and climatic parameters for several paddy grown areas (Ampara, Batticaloa, Badulla, Bandarawela, Hambantota, Trincomalee, Kurunegala, and Puttalam) with available data. Three training algorithms (Levenberg–Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugated Gradient (SCG)) are used to train the developed neural network model, and they are compared against each other to find the better training algorithm. Correlation coefficient (R) and Mean Squared Error (MSE) were used as the performance indicators to evaluate the performance of the developed ANN models. The results obtained from this study reveal that LM training algorithm has outperformed the other two algorithms in determining the relationships between climatic factors and paddy yield with less computational time. In addition, in the absence of seasonal climate data, annual prediction process is understood as an efficient prediction process. However, the results reveal that there is an error threshold in the prediction. Nevertheless, the obtained results are stable and acceptable under the highly unpredicted climate scenarios. The ANN relationships developed can be used to predict the future paddy yields in corresponding areas with the future climate data from various climate models.

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Section: Resultssupporting

confidence: 79%

Artificial Neural Network to Estimate the Paddy Yield Prediction Using Climatic Data

Amaratunga

Wickramasinghe

Perera

et al. 2020

Mathematical Problems in Engineering

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“…Its normalized root mean square error varied between 0.98 and 36.7%. Upland rice yield responses in Sahel, West Africa, were modeled to climate factors, by using several techniques, namely, MLR, boosted tree regression, and ANN [16]. As per the results, ANN outperformed the other two techniques and the research findings concluded that rainfall, not temperature, was the main climate driver of the rice yield in Sahel.…”

Section: Introductionmentioning

confidence: 87%

Modeling the Relationship between Rice Yield and Climate Variables Using Statistical and Machine Learning Techniques

Wickramasinghe

Weliwatta

Ekanayake

et al. 2021

Journal of Mathematics

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This paper presents the application of a multiple number of statistical methods and machine learning techniques to model the relationship between rice yield and climate variables of a major region in Sri Lanka, which contributes significantly to the country’s paddy harvest. Rainfall, temperature (minimum and maximum), evaporation, average wind speed (morning and evening), and sunshine hours are the climatic factors considered for modeling. Rice harvest and yield data over the last three decades and monthly climatic data were used to develop the prediction model by applying artificial neural networks (ANNs), support vector machine regression (SVMR), multiple linear regression (MLR), Gaussian process regression (GPR), power regression (PR), and robust regression (RR). The performance of each model was assessed in terms of the mean squared error (MSE), correlation coefficient (R), mean absolute percentage error (MAPE), root mean squared error ratio (RSR), BIAS value, and the Nash number, and it was found that the GPR-based model is the most accurate among them. Climate data collected until early 2019 (Maha season of year 2018) were used to develop the model, and an independent validation was performed by applying data of the Yala season of year 2019. The developed model can be used to forecast the future rice yield with very high accuracy.

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“…Given the current knowledge and technology, one of the problems is the selection of an appropriate learning and forecasting method adapted to a specific problem and data set. According to research by Zhang et al [171], the selection of the correct method of training neural networks and the method of forecasting the grain yield of rice has crucial effects on the accuracy of prediction. The study considered fields located in the northern, central and southern parts of Burkina Faso.…”

Section: Current Trends In Creating Forecasting Modelsmentioning

confidence: 99%

Selection of Independent Variables for Crop Yield Prediction Using Artificial Neural Network Models with Remote Sensing Data

Hara

Piekutowska

Niedbała

2021

Land

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Knowing the expected crop yield in the current growing season provides valuable information for farmers, policy makers, and food processing plants. One of the main benefits of using reliable forecasting tools is generating more income from grown crops. Information on the amount of crop yielding before harvesting helps to guide the adoption of an appropriate strategy for managing agricultural products. The difficulty in creating forecasting models is related to the appropriate selection of independent variables. Their proper selection requires a perfect knowledge of the research object. The following article presents and discusses the most commonly used independent variables in agricultural crop yield prediction modeling based on artificial neural networks (ANNs). Particular attention is paid to environmental variables, such as climatic data, air temperature, total precipitation, insolation, and soil parameters. The possibility of using plant productivity indices and vegetation indices, which are valuable predictors obtained due to the application of remote sensing techniques, are analyzed in detail. The paper emphasizes that the increasingly common use of remote sensing and photogrammetric tools enables the development of precision agriculture. In addition, some limitations in the application of certain input variables are specified, as well as further possibilities for the development of non-linear modeling, using artificial neural networks as a tool supporting the practical use of and improvement in precision farming techniques.

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Using boosted tree regression and artificial neural networks to forecast upland rice yield under climate change in Sahel

Cited by 51 publications

References 36 publications

Artificial Neural Network to Estimate the Paddy Yield Prediction Using Climatic Data

Artificial Neural Network to Estimate the Paddy Yield Prediction Using Climatic Data

Modeling the Relationship between Rice Yield and Climate Variables Using Statistical and Machine Learning Techniques

Selection of Independent Variables for Crop Yield Prediction Using Artificial Neural Network Models with Remote Sensing Data

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