scholarly journals Economic and climatic models for estimating coffee supply

2017 ◽  
Vol 52 (12) ◽  
pp. 1158-1166
Author(s):  
Adriana Ferreira de Moraes-Oliveira ◽  
Lucas Eduardo de Oliveira Aparecido ◽  
Sérgio Rangel Fernandes Figueira

Abstract: The objective of this work was to estimate the coffee supply by calibrating statistical models with economic and climatic variables for the main producing regions of the state of São Paulo, Brazil. The regions were Batatais, Caconde, Cássia dos Coqueiros, Cristais Paulista, Espírito Santo do Pinhal, Marília, Mococa, and Osvaldo Cruz. Data on coffee supply, economic variables (rural credit, rural agricultural credit, and production value), and climatic variables (air temperature, rainfall, potential evapotranspiration, water deficit, and water surplus) for each region, during the period from 2000-2014, were used. The models were calibrated using multiple linear regression, and all possible combinations were tested for selecting the variables. Coffee supply was the dependent variable, and the other ones were considered independent. The accuracy and precision of the models were assessed by the mean absolute percentage error and the adjusted coefficient of determination, respectively. The variables that most affect coffee supply are production value and air temperature. Coffee supply can be estimated with multiple linear regressions using economic and climatic variables. The most accurate models are those calibrated to estimate coffee supply for the regions of Cássia dos Coqueiros and Osvaldo Cruz.

2019 ◽  
Vol 35 (4) ◽  
Author(s):  
Raquel de Oliveira Santos ◽  
Rafael Coll Delgado ◽  
Marcos Gervasio Pereira ◽  
Leonardo Paula de Souza ◽  
Paulo Eduardo Teodoro ◽  
...  

The objective of this study was to evaluate the space-time dynamics of the soil use and occupation of the Rio Roncador river basin between 1985 and 2010. The scenes were classified by two methods (partially unsupervised - K-Means and supervised - Maximum likelihood), the Thematic Mapper sensor products on the LANDSAT 5 orbital platform were used for both images of a 25-year time series (1985 to 2000). In order to measure the accuracy of the field the computer application Google Earth was used, in which nine classes (urban area, agricultural area, pasture, exposed soil, native forest, secondary vegetation, mangrove, altitude field and water) were collected. A multiple linear regression was performed, correlating the Normalized Difference Vegetation Index - mean NDVI (dependent variable) with the independent climatic variables (global solar radiation - MJm-2day-1, average air temperature - °C, relative humidity -%, evapotranspiration - mm d-1, and rain - mm). According to the general classification by Kappa parameter of the images for 2005 and 2010, they were identified as very good (68% and 74%). These results confirm that the Roncador River Basin is undergoing transformation in its landscape, with an average reduction of -49% in native vegetation areas due to the increase in urban areas (25%) and agriculture (31%). The statistical analysis showed that rainfall and air temperature were the only variables that presented significant sigma (0.04) and (0.02). The obtained coefficient of determination indicated that 47% of the variations of the "vegetation index" are explained by the environmental variables.  


Irriga ◽  
2019 ◽  
Vol 1 (1) ◽  
pp. 38-47
Author(s):  
MARCO AURÉLIO ARGENTA MOCINHO JUNIOR ◽  
WILLIAN PEREIRA CENTURION ◽  
ARTHUR FERREIRA SOUZA PRADO ◽  
GUILHERME BOTEGA TORSONI ◽  
LUCAS EDUARDO DE OLIVEIRA APARECIDO ◽  
...  

MODELOS AGROMETEOROLÓGICOS PARA PREVISÃO DA PRODUÇÃO DE MILHO EM MATO GROSSO DO SUL     MARCO AURÉLIO ARGENTA MOCINHO JUNIOR1; WILLIAN PEREIRA CENTURION1; ARTHUR FERREIRA SOUZA PRADO1; GUILHERME BOTEGA TORSONI2; LUCAS EDUARDO DE OLIVEIRA APARECIDO2 E CICERO TEIXEIRA SILVA COSTA2   1Estudante do curso de Engenharia Agronômica do IFMS campus Naviraí. Laboratório de Engenharia Agrícola, Rua Hilda, 203, Naviraí - MS. CEP. 79950-000, E-mail: [email protected] 2Docentes do IFMS campus Naviraí. Rua Hilda,203, Naviraí-MS. CEP. 79950-000, E-mail: [email protected]     1 RESUMO   O milho representa um dos principais cereais cultivado e consumido no mundo, em virtude do seu alto potencial produtivo, composição química e valor nutritivo. No entanto, a sua produção é altamente dependente do clima. Objetivou-se estimar a produção do milho por meio da calibração de modelos estatísticos para o Estado de Mato Grosso do Sul - MS. As cidades estudadas foram Chapadão do Sul, Costa Rica, Ponta Porã e Sidrolândia. As variáveis climáticas utilizadas foram temperatura do ar, a precipitação pluvial, evapotranspiração potencial, déficit e o excesso hídrico no período de 2003 - 2017 entre fevereiro e maio. Os modelos foram calibrados e comparados pelos métodos KNN e RANDOM. A acurácia e a precisão dos modelos foram analisadas pelo erro percentual médio e pelo coeficiente de determinação ajustado, respectivamente. As variáveis que mais influenciaram na produção do milho foram o déficit hídrico e a temperatura do ar. É possível estimar a produção do milho com regressões lineares múltiplas utilizando variáveis climáticas. Chapadão do Sul e Costa Rica apresentam altos índices de déficit hídrico, enquanto Ponta Porã e Sidrolândia baixos déficits. O modelo mais acurado para estimar a produção do milho nas cidades foi o método RANDOM.   Keywords: Clima; Produtividade; Modelagem.     MOCINHO, M. A. A.; CENTURION, W. P; PRADO, A. F. S; TORSONI, G. B; APARECIDO, L. E. O; COSTA, C. T. S AGROMETEOROLOGICAL MODELS FOR FORECASTING MAIZE PRODUCTION IN MATO GROSSO DO SUL     2 ABSTRACT   Corn represents one of the main cereals cultivated and consumed in the world, due to its high productive potential, chemical composition and nutritional value. However, its production is highly climate dependent. The objective of this study was to estimate maize yield by calibrating statistical models for the state of Mato Grosso do Sul - MS. The cities studied were Chapadão do Sul, Costa Rica, Ponta Porã and Sidrolândia. The climatic variables used were air temperature, rainfall, potential evapotranspiration, deficit and excess water from 2003 to 2017 between February and May. The models were calibrated and compared by the KNN and RANDOM methods. The accuracy and precision of the models were analyzed by the mean percentage error and the adjusted determination coefficient, respectively. The variables that most influenced corn production were water deficit and air temperature. It is possible to estimate corn yield with multiple linear regressions using climate variables. Chapadão do Sul and Costa Rica have high levels of water deficit, while Ponta Porã and Sidrolândia have low deficits. The most accurate model for estimating maize yield in cities was the RANDOM method.   Keywords: Climate; Yield; Modeling.  


Irriga ◽  
2020 ◽  
Vol 25 (3) ◽  
pp. 641-655
Author(s):  
Paulo André da Silva Martins ◽  
Carlos Alexandre Santos Querino ◽  
Marcos Antônio Lima Moura ◽  
Juliane Kayse Albuquerque da Silva Querino ◽  
Leia Beatriz Vieira Bentolila ◽  
...  

BALANÇO HÍDRICO CLIMATOLÓGICO E CLASSIFICAÇÃO CLIMÁTICA DE THORNTHWAITE E MATHER (1955) PARA O MUNICÍPIO DE MANICORÉ, NA MESORREGIÃO SUL DO AMAZONAS     PAULO ANDRÉ DA SILVA MARTINS1; CARLOS ALEXANDRE SANTOS QUERINO2; MARCOS ANTÔNIO LIMA MOURA3; JULIANE KAYSE ALBUQUERQUE DA SILVA QUERINO4; LÉIA BEATRIZ VIEIRA BENTOLILA5 E PAULA CAROLINE DOS SANTOS SILVA6   1Doutorando em Geografia pela Universidade Federal de Rondônia -UNIR, membro pesquisador do grupo de pesquisa Interação biosfera atmosfera na Amazônia – GPIBA, da Universidade Federal do Amazonas – UFAM e grupo de pesquisa geografia e planejamento ambiental - LABOGEOPA, da Universidade Federal de Rondônia – UNIR, Rua 29 de agosto s/n, centro, CEP: 69800-000, Humaitá, Amazonas, Brasil. E-mail: [email protected] 2Departamento de Hidro meteorologia e pós-graduação em Ciências Ambientais da Universidade Federal do Amazonas – UFAM.  Rua 29 de agosto s/n, centro, CEP: 69800-000, Humaitá, Amazonas, Brasil. E-mail: [email protected] 3Instituto de Ciências Atmosféricas Universidade Federal de Alagoas – ICAT/UFAL. Avenida Lourival Melo Mota, S/N Tabuleiro dos Martins, CEP: 57072-900 Maceió, Alagoas, Brasil. E-mail: [email protected] 4Departamento de Hidro meteorologia e pós-graduação em Ciências Ambientais da Universidade Federal do Amazonas – UFAM.  Rua 29 de agosto s/n, centro, CEP: 69800-000, Humaitá, Amazonas, Brasil. E-mail: [email protected] 5Engenheira Ambiental, membra do grupo de pesquisa Interação biosfera atmosfera na Amazônia – GPIBA, da Universidade Federal do Amazonas – UFAM. Rua 29 de agosto s/n, centro, CEP: 69800-000, Humaitá, Amazonas, Brasil. E-mail:[email protected] 6Mestra em Ciências Ambientais pela Universidade Federal do Amazonas – UFAM. Membra do grupo de pesquisa Interação biosfera atmosfera na Amazônia – GPIBA, da Universidade Federal do Amazonas – UFAM Rua 29 de agosto s/n, centro, CEP: 69800-000, Humaitá, Amazonas, Brasil. E-mail:[email protected]     1 RESUMO   O padrão climático é descrito pelas condições das variáveis meteorológicas que exercem influência nas atividades humanas. Por sua vez, a agricultura é condicionada pela disponibilidade hídrica que pode ser conhecida através do balanço hídrico. Objetivou-se analisar a precipitação e a temperatura do ar, bem como realizar o balanço hídrico climatológico e a classificação climática em Manicoré-AM. Os dados foram coletados a partir da estação meteorológica do Instituto Nacional de Meteorologia entre os anos de 2010 a 2018. A evapotranspiração potencial foi calculada pelo modelo de Thornthwaite (1948). O balanço hídrico e a classificação climática foram estimados pela metodologia de Thornthwaite e Mather (1955). Os resultados foram analisados através de estatística descritiva. A precipitação média anual foi de 2.946,20 mm dos quais 90% ocorreram no período chuvoso. A temperatura do ar (Tar) média anual variou entre 25 e 27 °C. A deficiência hídrica anual média foi de 267,91 mm entre maio e setembro. O excedente hídrico médio anual foi de 1.609,26 mm entre dezembro e abril. A evapotranspiração potencial média anual foi de 1.604,85 mm, com máxima em agosto e mínima em julho. Por fim, a Classificação climática foi AwA’a’, clima super úmido megatérmico com moderada deficiência hídrica no inverno.   Palavras-Chaves: Precipitação, Temperatura do ar, Padrão climático.   MARTINS, P. A. da S.; QUERINO, C. A. S.; MOURA, MARCOS A. L.; QUERINO, J. K. A. da S.; BENTOLILA, L. B. V.; SILVA, P. C. dos S. CLIMATIC WATER BALANCE AND THORNTHWAITE AND MATHER (1955) CLIMATE CLASSIFICATION FOR MANICORÉ MUNICIPALITY IN AMAZONAS SOUTH MESOREGION     2 ABSTRACT   Climate pattern can be described by the conditions of the meteorological variables that exert influence on human activities. Agriculture, in its turn, is conditioned by water availability, which can be known through water balance. This paper aimed to analyze precipitation and air temperature, as well as to perform the climatic water balance and climatic classification in the municipality of Manicoré (Amazonas State, Brazil). Data were collected from the meteorological station of the National Institute of Meteorology from 2010 through 2018. Potential evapotranspiration was calculated by the Thornthwaite model (Thornthwaite, 1948). Water balance and climatic classification were estimated by Thornthwaite and Mather (1955) methodology. The results were analyzed with descriptive statistics. The mean annual precipitation was 2.946.20 mm, of which 90% occurred in the rainy season. The average annual air temperature ranged from 25 to 27 ° C. The mean annual water deficit was 267.91 mm from May through September. The average annual water surplus was 1,609.26 mm from December through April. The annual average potential evapotranspiration was 1,604.85 mm, with maximum in August and minimum in July. Finally, the climatic classification was AwA'a ', super humid megathermal climate with moderate water deficiency in winter.   Keywords: Precipitation, Air temperature, Southern Amazonas.


Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3973
Author(s):  
Gaia Codeluppi ◽  
Luca Davoli ◽  
Gianluigi Ferrari

With the advent of the Smart Agriculture, the joint utilization of Internet of Things (IoT) and Machine Learning (ML) holds the promise to significantly improve agricultural production and sustainability. In this paper, the design of a Neural Network (NN)-based prediction model of a greenhouse’s internal air temperature, to be deployed and run on an edge device with constrained capabilities, is investigated. The model relies on a time series-oriented approach, taking as input variables the past and present values of the air temperature to forecast the future ones. In detail, we evaluate three different NN architecture types—namely, Long Short-Term Memory (LSTM) networks, Recurrent NNs (RNNs) and Artificial NNs (ANNs)—with various values of the sliding window associated with input data. Experimental results show that the three best-performing models have a Root Mean Squared Error (RMSE) value in the range 0.289÷0.402∘C, a Mean Absolute Percentage Error (MAPE) in the range of 0.87÷1.04%, and a coefficient of determination (R2) not smaller than 0.997. The overall best performing model, based on an ANN, has a good prediction performance together with low computational and architectural complexities (evaluated on the basis of the NetScore metric), making its deployment on an edge device feasible.


2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Shengpu Li ◽  
Yize Sun

Ink transfer rate (ITR) is a reference index to measure the quality of 3D additive printing. In this study, an ink transfer rate prediction model is proposed by applying the least squares support vector machine (LSSVM). In addition, enhanced garden balsam optimization (EGBO) is used for selection and optimization of hyperparameters that are embedded in the LSSVM model. 102 sets of experimental sample data have been collected from the production line to train and test the hybrid prediction model. Experimental results show that the coefficient of determination (R2) for the introduced model is equal to 0.8476, the root-mean-square error (RMSE) is 6.6 × 10 (−3), and the mean absolute percentage error (MAPE) is 1.6502 × 10 (−3) for the ink transfer rate of 3D additive printing.


2021 ◽  
Vol 149 ◽  
Author(s):  
Junwen Tao ◽  
Yue Ma ◽  
Xuefei Zhuang ◽  
Qiang Lv ◽  
Yaqiong Liu ◽  
...  

Abstract This study proposed a novel ensemble analysis strategy to improve hand, foot and mouth disease (HFMD) prediction by integrating environmental data. The approach began by establishing a vector autoregressive model (VAR). Then, a dynamic Bayesian networks (DBN) model was used for variable selection of environmental factors. Finally, a VAR model with constraints (CVAR) was established for predicting the incidence of HFMD in Chengdu city from 2011 to 2017. DBN showed that temperature was related to HFMD at lags 1 and 2. Humidity, wind speed, sunshine, PM10, SO2 and NO2 were related to HFMD at lag 2. Compared with the autoregressive integrated moving average model with external variables (ARIMAX), the CVAR model had a higher coefficient of determination (R2, average difference: + 2.11%; t = 6.2051, P = 0.0003 < 0.05), a lower root mean-squared error (−24.88%; t = −5.2898, P = 0.0007 < 0.05) and a lower mean absolute percentage error (−16.69%; t = −4.3647, P = 0.0024 < 0.05). The accuracy of predicting the time-series shape was 88.16% for the CVAR model and 86.41% for ARIMAX. The CVAR model performed better in terms of variable selection, model interpretation and prediction. Therefore, it could be used by health authorities to identify potential HFMD outbreaks and develop disease control measures.


Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4655
Author(s):  
Dariusz Czerwinski ◽  
Jakub Gęca ◽  
Krzysztof Kolano

In this article, the authors propose two models for BLDC motor winding temperature estimation using machine learning methods. For the purposes of the research, measurements were made for over 160 h of motor operation, and then, they were preprocessed. The algorithms of linear regression, ElasticNet, stochastic gradient descent regressor, support vector machines, decision trees, and AdaBoost were used for predictive modeling. The ability of the models to generalize was achieved by hyperparameter tuning with the use of cross-validation. The conducted research led to promising results of the winding temperature estimation accuracy. In the case of sensorless temperature prediction (model 1), the mean absolute percentage error MAPE was below 4.5% and the coefficient of determination R2 was above 0.909. In addition, the extension of the model with the temperature measurement on the casing (model 2) allowed reducing the error value to about 1% and increasing R2 to 0.990. The results obtained for the first proposed model show that the overheating protection of the motor can be ensured without direct temperature measurement. In addition, the introduction of a simple casing temperature measurement system allows for an estimation with accuracy suitable for compensating the motor output torque changes related to temperature.


Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1166
Author(s):  
Bashir Musa ◽  
Nasser Yimen ◽  
Sani Isah Abba ◽  
Humphrey Hugh Adun ◽  
Mustafa Dagbasi

The prediction accuracy of support vector regression (SVR) is highly influenced by a kernel function. However, its performance suffers on large datasets, and this could be attributed to the computational limitations of kernel learning. To tackle this problem, this paper combines SVR with the emerging Harris hawks optimization (HHO) and particle swarm optimization (PSO) algorithms to form two hybrid SVR algorithms, SVR-HHO and SVR-PSO. Both the two proposed algorithms and traditional SVR were applied to load forecasting in four different states of Nigeria. The correlation coefficient (R), coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were used as indicators to evaluate the prediction accuracy of the algorithms. The results reveal that there is an increase in performance for both SVR-HHO and SVR-PSO over traditional SVR. SVR-HHO has the highest R2 values of 0.9951, 0.8963, 0.9951, and 0.9313, the lowest MSE values of 0.0002, 0.0070, 0.0002, and 0.0080, and the lowest MAPE values of 0.1311, 0.1452, 0.0599, and 0.1817, respectively, for Kano, Abuja, Niger, and Lagos State. The results of SVR-HHO also prove more advantageous over SVR-PSO in all the states concerning load forecasting skills. This paper also designed a hybrid renewable energy system (HRES) that consists of solar photovoltaic (PV) panels, wind turbines, and batteries. As inputs, the system used solar radiation, temperature, wind speed, and the predicted load demands by SVR-HHO in all the states. The system was optimized by using the PSO algorithm to obtain the optimal configuration of the HRES that will satisfy all constraints at the minimum cost.


2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Boluwaji M. Olomiyesan ◽  
Onyedi D. Oyedum

In this study, the performance of three global solar radiation models and the accuracy of global solar radiation data derived from three sources were compared. Twenty-two years (1984–2005) of surface meteorological data consisting of monthly mean daily sunshine duration, minimum and maximum temperatures, and global solar radiation collected from the Nigerian Meteorological (NIMET) Agency, Oshodi, Lagos, and the National Aeronautics Space Agency (NASA) for three locations in North-Western region of Nigeria were used. A new model incorporating Garcia model into Angstrom-Prescott model was proposed for estimating global radiation in Nigeria. The performances of the models used were determined by using mean bias error (MBE), mean percentage error (MPE), root mean square error (RMSE), and coefficient of determination (R2). Based on the statistical error indices, the proposed model was found to have the best accuracy with the least RMSE values (0.376 for Sokoto, 0.463 for Kaduna, and 0.449 for Kano) and highest coefficient of determination, R2 values of 0.922, 0.938, and 0.961 for Sokoto, Kano, and Kaduna, respectively. Also, the comparative study result indicates that the estimated global radiation from the proposed model has a better error range and fits the ground measured data better than the satellite-derived data.


2019 ◽  
Vol 60 (5) ◽  
pp. 1037-1048
Author(s):  
Hussein Ilaibi Zamil Al-Sudani

     In any natural area or water body, evapotranspiration is one of the important outcomes in the water balance equation. As a significant method and depending on monthly average temperature, estimating of potential Evapotranspiration depending on Thornthwaite method was adopted in this research review. Estimate and discuss evapotranspiration by using Thornthwaite method is the main objectives of this research review with considerable details as well as compute potential evapotranspiration based on climatologically data obtained in Iraq. Temperature - evapotranspiration relationship can be estimated between those two parameters to reduce cost and time and facilitate calculation of water balance in lakes, river, and hydrogeological basins. The relationship was obtained using Thornthwaite method in Iraq by dividing the area into seven sectors according to geographic latitude. Each sector has multi meteorological stations where thirty two stations were used with different periods of records. A mathematical relationship was obtained between mean temperature and corrected potential evapotranspiration with (97.45) to (99.84) coefficient of determination. The mean temperature has a decreasing pattern from southern east towards northern west of Iraq affected by Mediterranean Sea climate conditions, while corrected potential evapotranspiration has the opposite direction regarding increased value because of a direct relationship with temperature.


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