Extraction/exportation of macronutrients by cladodes of ‘Gigante’ cactus pear under different spacings and organic fertilization

ABSTRACT This study aimed to evaluate the extraction/exportation of macronutrients by cladodes of ‘Gigante’ cactos pear, grown under diferente spacings and doses of cattle manure applied to the soil 600 days after planting. Twelve treatments were used, three spacing (1.00 x 0.50; 2.00 x 0.25 and 3.00 x 1.00 x 0.25 m) and four doses of cattle manure (0, 30, 60 and 90 Mg ha-1 year-1), arranged in a 3 x 4 factorial scheme in randomized blocks, with three replicates. The extraction/exportation of N, P, K, S, Ca and Mg was determined, whose respective values for maximum dry matter production (21.8 Mg ha-1), with a cattle manure dose of 71.8 Mg ha-1 year-1 were: 287.9, 46.2, 924.2, 40.7, 609.7 and 249.1 kg ha-1, 600 days after planting. The amounts extracted/exported from N, P, K and Ca varied independently with spacings and manure doses, while Mg and S were dependent on the interaction between factors. The doses of manure are insufficient to meet the demand of extracted/exported K, Ca and Mg. The increments in the doses promote greater nutrient uptake by the plant. The extracted/exported macronutrients in largest amounts are: K, Ca, N, Mg, P and S, in this order

Yield and vegetative growth of cactus pear at different spacings and under chemical fertilizations

ABSTRACT The objective was to evaluate the effect of different spacings and mineral fertilizations on cactus pear growth and production in a randomized block design, with three replicates, in a 3 x 4 factorial scheme: three spacings, 1.00 x 0.50 m, 2.00 x 0.25 m and 3.00 x 1.00 x 0.25 m, and four fertilizations, 000-000-000, 000-150-000, 200-150-000 and 200-150-100 kg ha-1 of N, P2O5 and K2O, respectively. Plant growth was evaluated between 90 and 390 days and production and growth were evaluated at 620 days after planting. There were significant interactions between spacing and fertilization for plant height, number of cladodes and cladode area index from 90 to 390 days and for production of fresh and dry matter at 620 days after planting. Spacing influenced cladode area index, while fertilization influenced plant height, number of cladodes and cladode area index at 620 days after planting. Plant height showed cubic effect for the days after planting. Number of cladodes and cladode area index were dependent on spacing, fertilization and plant age, and fitted to cubic models. The best results of growth and production of fresh and dry matter are associated with NPK and NP fertilizations and the spacing of 1.00 x 0.50 m

Extraction/export of nutrients in Opuntia ficus-indica under different spacings and chemical fertilizers

ABSTRACT This work aimed to evaluate extraction/ export of nutrients and dry matter production in the 'Gigante' cactus pear, grown in different spacings and fertilizations 620 days after planting. Twelve combination of treatments were used consisting of: three spacings - 1.00 x 0.50; 2.00 x 0.25; and 3.00 x 1.00 x 0.25 m, and four fertilizations - 000-000-000; 000-150-000; 200-150-000; and 200-150-100, kg ha-1, of N, P2O5 and K2O, in a 3 x 4 factorial scheme in a randomized block design, with three replicates. Extraction/export of N, P, K, S, Ca, Mg, B, Fe, Mn, Zn, Na and Cu were determined and the means were 304.35; 18.81; 421.04; 62.35; 464.63; 215.77; 0.39; 0.81; 23.74; 1.11; 0.62 and 0.08 kg ha-1, besides the mean dry matter production of 17.11 Mg ha-1. There were significant interactions for extraction/export of Mg and dry matter production. The fertilizations used were insufficient to meet the demand of N, K, Ca, Mg, S and micronutrients. Fertilization increased the extraction of nutrients, particularly N, P and S at the spacing of 1.00 x 0.50 m, and increased dry matter production. The decreasing order of extraction/export was Ca, K, N, Mg, S and P for macronutrients and Mn, Zn, Fe, Na, B and Cu for micronutrients

Prediction of ‘Gigante’ cactus pear yield by morphological characters and artificial neural networks

ABSTRACT Estimating cactus pear yield is important for the planning of small and medium rural producers, especially in environments with adverse climatic conditions, such as the Brazilian semi-arid region. The objective of this study was to evaluate the potential of artificial neural networks (ANN) for predicting yield of ‘Gigante’ cactus pear, and determine the most important morphological characters for this prediction. The experiment was conducted in the Instituto Federal Baiano, Guanambi campus, Bahia, Brazil, in 2009 to 2011. The area used is located at 14° 13’ 30” S and 42° 46’ 53” W, and its altitude is 525 m. Six vegetative agronomic characters were evaluated in 500 plants in the third production cycle. The data were subjected to ANN analysis using the R software. Ten network architectures were trained 100 times to select the one with the lowest mean square error for the validation data. The networks with five neurons in the middle layer presented the best results. Neural networks with coefficient of determination (R2) of 0.87 were adjusted for sample validation, assuring the generalization potential of the model. The morphological characters with the highest relative contribution to yield estimate were total cladode area, plant height, cladode thickness and cladode length, but all characters were important for predicting the cactus pear yield. Therefore, predicting the production of cactus pear with high precision using ANN and morphological characters is possible

Prediction of ‘Gigante’ cactus pear yield by morphological characters and artificial neural networks

<div><p>ABSTRACT Estimating cactus pear yield is important for the planning of small and medium rural producers, especially in environments with adverse climatic conditions, such as the Brazilian semi-arid region. The objective of this study was to evaluate the potential of artificial neural networks (ANN) for predicting yield of ‘Gigante’ cactus pear, and determine the most important morphological characters for this prediction. The experiment was conducted in the Instituto Federal Baiano, Guanambi campus, Bahia, Brazil, in 2009 to 2011. The area used is located at 14° 13’ 30” S and 42° 46’ 53” W, and its altitude is 525 m. Six vegetative agronomic characters were evaluated in 500 plants in the third production cycle. The data were subjected to ANN analysis using the R software. Ten network architectures were trained 100 times to select the one with the lowest mean square error for the validation data. The networks with five neurons in the middle layer presented the best results. Neural networks with coefficient of determination (R2) of 0.87 were adjusted for sample validation, assuring the generalization potential of the model. The morphological characters with the highest relative contribution to yield estimate were total cladode area, plant height, cladode thickness and cladode length, but all characters were important for predicting the cactus pear yield. Therefore, predicting the production of cactus pear with high precision using ANN and morphological characters is possible.</p></div