Dataset Information

Evaluating the Effect of Three Water Management Techniques on Tomato Crop.

ABSTRACT: The effects of three water management techniques were evaluated on subsurface drip irrigated tomatoes. The three techniques were the intermittent flow (3 pulses), the dual-lateral drip system (two lateral lines per row, at 15 and 25 cm below soil surface), and the physical barrier (buried at 30 cm below soil surface). Field experiments were established for two successive seasons. Water movement in soil was monitored using continuously logging capacitance probes up to 60 cm depth. The results showed that the dual lateral technique positively increased the yield up to 50%, water use efficiency up to 54%, while the intermittent application improved some of the quality measures (fruit size, TSS, and Vitamin C), not the quantity of the yield that decreased in one season, and not affected in the other. The physical barrier has no significant effect on any of the important growth measures. The soil water patterns showed that the dual lateral method lead to uniform wetting pattern with depth up to 45 cm, the physical barrier appeared to increase lateral and upward water movement, while the intermittent application kept the wetting pattern at higher moisture level for longer time. The cost analysis showed also that the economic treatments were the dual lateral followed by the intermittent technique, while the physical barrier is not economical. The study recommends researching the effect of the dual lateral method on the root growth and performance. The intermittent application may be recommended to improve tomato quality but not quantity. The physical barrier is not recommended unless in high permeable soils.

SUBMITTER: Elnesr MN

PROVIDER: S-EPMC4461261 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Evaluating the Effect of Three Water Management Techniques on Tomato Crop.

Elnesr Mohammad Nabil MN Alazba Abdurrahman Ali AA Zein El-Abedein Assem Ibrahim AI El-Adl Mahmoud Maher MM

PloS one 20150609 6

The effects of three water management techniques were evaluated on subsurface drip irrigated tomatoes. The three techniques were the intermittent flow (3 pulses), the dual-lateral drip system (two lateral lines per row, at 15 and 25 cm below soil surface), and the physical barrier (buried at 30 cm below soil surface). Field experiments were established for two successive seasons. Water movement in soil was monitored using continuously logging capacitance probes up to 60 cm depth. The results sho ...[more]

PMID: 26057380

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Project description:Soil CO2 emissions are regarded as one of the largest flows of the global carbon cycle and small changes in their magnitude can have a large effect on the CO2 concentration in the atmosphere. Thus, a better understanding of this attribute would enable the identification of promoters and the development of strategies to mitigate the risks of climate change. Therefore, our study aimed at using data mining techniques to predict the soil CO2 emission induced by crop management in sugarcane areas in Brazil. To do so, we used different variable selection methods (correlation, chi-square, wrapper) and classification (Decision tree, Bayesian models, neural networks, support vector machine, bagging with logistic regression), and finally we tested the efficiency of different approaches through the Receiver Operating Characteristic (ROC) curve. The original dataset consisted of 19 variables (18 independent variables and one dependent (or response) variable). The association between cover crop and minimum tillage are effective strategies to promote the mitigation of soil CO2 emissions, in which the average CO2 emissions are 63 kg ha-1 day-1. The variables soil moisture, soil temperature (Ts), rainfall, pH, and organic carbon were most frequently selected for soil CO2 emission classification using different methods for attribute selection. According to the results of the ROC curve, the best approaches for soil CO2 emission classification were the following: (I)-the Multilayer Perceptron classifier with attribute selection through the wrapper method, that presented rate of false positive of 13,50%, true positive of 94,20% area under the curve (AUC) of 89,90% (II)-the Bagging classifier with logistic regression with attribute selection through the Chi-square method, that presented rate of false positive of 13,50%, true positive of 94,20% AUC of 89,90%. However, the (I) approach stands out in relation to (II) for its higher positive class accuracy (high CO2 emission) and lower computational cost.

Dataset Information

Evaluating the Effect of Three Water Management Techniques on Tomato Crop.

Publications

Evaluating the Effect of Three Water Management Techniques on Tomato Crop.

Similar Datasets

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