We evaluated the effect of different soil water tensions on the production of broccoli cultivated in a protected environment under drip irrigation in order to establish criteria for the adequate management of irrigation. A completely randomized block design was used, comprising six treatments and four replicates. The treatments included six soil water tensions (15, 30, 45, 60, 75 and 90 kPa). Soil water tension was monitored with granular matrix sensors installed at depths of 0.2 m (decision sensors) and 0.4 m (seepage control sensors). Total and marketable fresh weight of broccoli heads, average diameter of marketable heads, height of marketable heads, and total and marketable yield were greatest when the soil water tension at a depth of 0.2 m was 15 kPa, at which the mean values of the evaluated variables were 0.84 kg, 0.76 kg, 20.5 cm, 11.7 cm; 26.5 t ha -1 , and 23.7 t ha -1 , respectively. Treatments did not significantly affect efficiency of water use or height of marketable heads. Key words: Scheduling irrigation, vegetable crop, protected environment ResumoObjetivou-se, avaliar o efeito de diferentes tensões de água no solo sobre a produção de brócolis, cultivado em ambiente protegido e irrigado por gotejamento, de forma a estabelecer critérios para o manejo adequado da irrigação. O experimento foi conduzido na Universidade Federal de Lavras, no período de Maio a Agosto de 2012. O delineamento experimental foi em blocos completos casualizados, com seis tratamentos e quatro repetições. Os tratamentos foram constituídos de seis tensões de água no solo (15, 30, 45, 60, 75 e 90 kPa). As tensões de água no solo foram monitoradas com base nos Sensores de Matriz Granular, watermark® instalados a 0,2 e a 0,4 m de profundidade. Dos resultados, concluiu-se que para a obtenção de maiores valores de massa fresca total e comercial, diâmetro médio da inflorescência, altura da inflorescência, produtividade total e comercial, as irrigações devem ser realizadas quando a tensão de água no solo estiver em torno de 15 kPa, à uma profundidade de 0,2 m. Os maiores valores atingidos foram de 0,84 kg; 0,76 kg; 20,5 cm; 11,5 cm; 26,47 t ha -1 e 23,71 t ha -1 , respectivamente. A variação da tensão de água no solo não produziu efeito significativo na eficiência no uso de água e na altura da inflorescência comercial. Palavras-chave: Manejo da irrigação, olerícola, ambiente protegido
; R 2 = 0.95 for S6 (SVM). A6 and S6 architectures were composed of maximum temperature (T max.), minimum (Tmin.), average temperature (T), extraterrestrial radiation (Ra) and Rs. The HS method was the worst method in terms of performance, while AB method had the best results than A1 and S1, which only used T.
REDES NEURAIS ARTIFICIAIS, REGRESSÃO E MÉTODOS EMPÍRICOS PARA A MODELAGEM DA EVAPOTRANSPIRAÇÃO DE REFERÊNCIA NA CIDADE DE INHAMBANE, MOÇAMBIQUE BARTOLOMEU FÉLIX TANGUNE1 E RODRIGO MÁXIMO SÁNCHEZ ROMÁN2 1 Departamento de Engenharia Rural, Escola Superior de Desenvolvimento Rural, Universidade Eduardo Mondlane, Vilankulo, Inhambane, Moçambique. E-mail: tanguneb@gmail.com. 2 Departamento de Engenharia Rural, Faculdade de Ciências Agronômicas, Universidade Estadual Paulista (UNESP) Campus de Botucatu. Fazenda Experimental Lageado, Avenida Universitária, nº 3780, Altos do Paraíso, CEP: 18610-034, Botucatu – SP. Brasil. E-mail: rodrigo.roman@unesp.br 1 RESUMO Estimativa precisa da evapotranspiração de referência (ETo) é importante para dimensionar e fazer manejo de sistemas de irrigação. Métodos de estimativa da ETo (11 métodos empíricos, 10 modelos de regressão múltipla: RLM e 10 redes neurais artificias: RNAs) foram avaliados em relação ao método padrão de Penman Monteith FAO 56, utilizando os seguintes índices: MBE (Mean Bias Error), RMSE (Root Mean Square Error) e R2, sendo RMSE utilizado como critério principal de seleção dos métodos. A significância dos métodos foi avaliada com base no teste t utilizando dados de 1985 a 2009. Os dados meteorológicos utilizados (temperatura máxima: Tmax, temperatura mínima: Tmin e temperatura média: T, umidade relativa, velocidade do vento e insolação) são da estação meteorológica convencional da cidade de Inhambane, Moçambique. Os resultados mostraram que o modelo RLM4 apresentou melhor desempenho (MBE = 0,01 mm.d-1; RMSE = 0,15 mm.d-1; R2 = 0,99). Na falta da radiação solar global, os modelos RLM6 (MBE = -0,01 mm.d-1; RMSE = 0,23 mm.d-1; R2 = 0,97) e RLM10 (MBE = 0,01 mm.d-1; RMSE = 0,23 mm.d-1; R2 = 0,97) podem ser utilizados e exigem a medição da T, Tmax e Tmin, respectivamente. Esses modelos não foram estatisticamente diferentes do método padrão. Palavras-chave: evapotranspiração, regressão múltipla, redes neurais. TANGUNE, B. F.; SÁNCHEZ-ROMÁN, R. M. ARTIFICIAL NEURAL NETWORKS, REGRESSION AND EMPIRICAL METHODS FOR REFERENCE EVAPOTRANSPIRATION MODELING IN INHAMBANE CITY, MOZAMBIQUE 2 ABSTRACT Precise estimation of reference evapotranspiration (ETo) is important for designing and managing irrigation systems. Methods of ETo estimation (11 empirical methods, 10 multiple regression models: RLM and 10 artificial neural networks: RNAs) were evaluated against Penman Monteith FAO 56 method using the following indexes: MBE (Mean Bias Error), RMSE (Root Mean Square Error) and R2, and RMSE was used as the main criterion of method selection. The significance of the methods was evaluated on the basis of the t test using data from 1985 to 2009. The meteorological data used (maximum temperature: Tmax, minimum temperature: Tmin and average temperature: T, relative air humidity, wind speed and solar brightness), from 1985 to 2009, are from the conventional meteorological station of the city of Inhambane, Mozambique. The results showed that the RLM4 model presented better performance (MBE = 0.01 mm.d-1; RMSE = 0.15 mm.d-1; R2 = 0.99). In the absence of global solar radiation, RLM6 (MBE = -0.01 mm.d-1; RMSE = 0.23 mm.d-1; R2 = 0.97) and RLM10 (MBE = 0.01 mm. d-1; RMSE = 0.23 mm.d-1; R2 = 0.97) can be used, which require measurement of T, and Tmax and Tmin, respectively. These models were not statistically different from the standard method. Keywords: evapotranspiration, multiple regression, neural networks.
Reference evapotranspiration (ETo) is useful for water management, calculating crop water requirements and irrigation scheduling. ETo was estimated from 5 empirical methods based on temperature, 5 based on solar radiation and on Machine Learning Technique (MLT). The MLT model consisted of Artificial Neural Networks (ANNs) and Support Vector Machine (SVM), with 6 architectures each. The MLT and empirical methods were tested against the Penman Monteith FAO 56 method based on the following statistical parameters: MBE (Mean Bias Error), RMSE (Mean Square Root Error), d (coefficient of Willmott) and R2 (coefficient of determination). The meteorological data used (maximum temperature, low and average temperature, relative humidity, wind speed and sunshine hours: n) were obtained from the National Institute of Meteorology of Mozambique. The results obtained from the modeling showed the following: Jones and Ritchie (JRICH) > Makkink, SVM3 > SVM6 > SVM1 > SVM2 = SVM4 > SVM5 > ANN5> Abtew > Hargreaves – Samani > ANN1 = ANN6 > ANN4 > Irmak > ANN3 > Jensen Haise > ANN2 > Blaney Criddle Original > Schendel > Kharrufa > Mc Guinness-Bordne. Global solar radiation, which is one of the variables needed for the JRICH method (MBE = -0.17 mm d-1; RMSE = 0.38 mm d-1; d = 0.98 and R2 = 0.98) is not always measured or calculated. In this case, SVM1could be used since it only requires measurements of T (MBE = 0.16 mm d-1; RMSE = 0.62 mm d-1; d = 0.94 and R2 = 0.83).
Grid and quadrants size selection to determine crop density and spatial variability are still very controversial, either when accessing isolated sampling factors, either, combining both. Thus, this work aimed to evaluate the ideal sampling grid and quadrant size to determine the crop density and spatial variability of African pumpkin (Momordica balsamina. L.). The trial was conducted in Pambara, Vilankulo district, Mozambique, based on the factorial scheme, on a completely randomized design, comprising four levels of each factor: grid: 10 x 10 m 2, 20 x 20 m 2, 30 x 30 m 2, 40 x 40 m 2; quadrant: 1 x 1 m 2, 0.75 x 0.75 m 2, 0.5 x 0.5 m 2, 0.25 x 0.25 m 2, comprising 16 treatments and 928 georeferenced samples. To evaluate the trial, crop density data were collected, and then submitted to Normality test, Analysis of Variance, mean test at 5% significance, Geostatistics and multivariate. The results showed that the combination of the grid 20 x 20 m 2 with the quadrant 0.25 x 0.25 m 2 was ideal for determining the crop density. The principal component analysis showed that only two components contain 66,5% of the total information.
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