Evidence map›Paper›PMID 36597441›Full record

ArticleApplied water science2023

An intelligent approach to improve date palm crop yield and water productivity under different irrigation and climate scenarios.

Hossein Dehghanisanij, Nader Salamati, Somayeh Emami, Hojjat Emami, Haruyuki Fujimaki

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Applied water science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Article
  3. An Overview of Date (Foods (Basel, Switzerland) · 2024
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Hossein DehghanisanijAgricultural Research, Education and Extension Organization, Agricultural Engineering Research Institute, Karaj, P.O. Box 31585-845, Alborz Iran.ORCID 0000-0002-5617-9375
Nader SalamatiAgricultural Research, Education and Extension Organization, Khuzestan Agricultural and Natural Resources Research and Education Center, Ahvaz, Iran.
Somayeh EmamiDepartment of Water Engineering, University of Tabriz, Tabriz, Iran.
Hojjat EmamiDepartment of Computer Engineering, University of Bonab, Bonab, Iran.
Haruyuki FujimakiArid Land Research Center, Tottori University, Tottori, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Drought, rising demand for water, declining water resources, and mismanagement have put society at serious risk. Therefore, it is essential to provide appropriate solutions to increase water productivity (WP). As an element of research, this study presents a hybrid machine learning approach and investigates its potential for estimating date palm crop yield and WP under different levels of subsurface drip irrigation (SDI). The amount of applied water in the SDI system was compared at three levels of 125% (

Indexed as

ACVO algorithmACVO-ANFISDate palmSubsurface drip irrigation (SDI)Water productivityYield

Identifiers

PMID36597441
PMCPMC9801156

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.