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A decision support system (GesCoN) for managing fertigation in open field vegetable crops. Part I—methodological approach and description of the software

Overview of attention for article published in Frontiers in Plant Science, May 2015
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Title
A decision support system (GesCoN) for managing fertigation in open field vegetable crops. Part I—methodological approach and description of the software
Published in
Frontiers in Plant Science, May 2015
DOI 10.3389/fpls.2015.00319
Pubmed ID
Authors

Antonio Elia, Giulia Conversa

Abstract

Reduced water availability and environmental pollution caused by nitrogen (N) losses have increased the need for rational management of irrigation and N fertilization in horticultural systems. Decision support systems (DSS) could be powerful tools to assist farmers to improve irrigation and N fertilization efficiency. Currently, fertilization by drip irrigation system (fertigation) is used for many vegetable crops around the world. The paper illustrates the theoretical basis, the methodological approach and the structure of a DSS called GesCoN for fertigation management in open field vegetable crops. The DSS is based on daily water and N balance, considering the water lost by evapotranspiration (ET) and the N content in the aerial part of the crop (N uptake) as subtraction and the availability of water and N in the wet soil volume most effected by roots as the positive part. For the water balance, reference ET can be estimated using the Penman-Monteith (PM) or the Priestley-Taylor and Hargreaves models, specifically calibrated under local conditions. Both single or dual Kc approach can be used to calculate crop ET. Rain runoff and deep percolation are considered to calculate the effective rainfall. The soil volume most affected by the roots, the wet soil under emitters and their interactions are modeled. Crop growth is modeled by a non-linear logistic function on the basis of thermal time, but the model takes into account thermal and water stresses and allows an in-season calibration through a dynamic adaptation of the growth rate to the specific genetic and environmental conditions. N crop demand is related to DM accumulation by the N critical curve. N mineralization from soil organic matter is daily estimated. The DSS helps users to evaluate the daily amount of water and N fertilizer that has to be applied in order to fulfill the water and N-crop requirements to achieve the maximum potential yield, while reducing the risk of nitrate outflows.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 2%
Unknown 64 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 20%
Student > Master 11 17%
Student > Ph. D. Student 10 15%
Student > Bachelor 4 6%
Professor 3 5%
Other 9 14%
Unknown 15 23%
Readers by discipline Count As %
Agricultural and Biological Sciences 25 38%
Engineering 9 14%
Environmental Science 6 9%
Business, Management and Accounting 2 3%
Earth and Planetary Sciences 2 3%
Other 5 8%
Unknown 16 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 June 2015.
All research outputs
#20,276,249
of 22,808,725 outputs
Outputs from Frontiers in Plant Science
#15,986
of 20,093 outputs
Outputs of similar age
#223,232
of 266,611 outputs
Outputs of similar age from Frontiers in Plant Science
#220
of 275 outputs
Altmetric has tracked 22,808,725 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 20,093 research outputs from this source. They receive a mean Attention Score of 4.0. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 266,611 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 275 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.