Evidence map›Paper›PMID 35336688›Full record

ArticlePlants (Basel, Switzerland)2022

Crop Model Parameterisation of Three Important Pearl Millet Varieties for Improved Water Use and Yield Estimation.

Petrus A Ausiku, John G Annandale, Joachim Martin Steyn, Andrew J Sanewe

Abstract read
In one paragraph

Article in Plants (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

4 authors.

Petrus A AusikuDepartment of Plant and Soil Sciences, University of Pretoria, Private Bag X20, Pretoria 0028, South Africa.ORCID 0000-0001-7646-677X
John G AnnandaleDepartment of Plant and Soil Sciences, University of Pretoria, Private Bag X20, Pretoria 0028, South Africa.ORCID 0000-0002-2942-6748
Joachim Martin SteynDepartment of Plant and Soil Sciences, University of Pretoria, Private Bag X20, Pretoria 0028, South Africa.ORCID 0000-0002-9959-3234
Andrew J SaneweDepartment of Plant and Soil Sciences, University of Pretoria, Private Bag X20, Pretoria 0028, South Africa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pearl millet is an important crop for food security in Asia and Africa's arid and semi-arid regions. It is widely grown as a staple cereal grain for human consumption and livestock fodder. Mechanistic crop growth and water balance models are useful to forecast crop production and water use. However, very few studies have been devoted to the development of the model parameters needed for such simulations for pearl millet. The objectives of the study were to determine crop-specific model parameters for each of three pearl millet varieties (landrace, hybrid, and improved), as well as to calibrate and validate the Soil Water Balance (SWB) model for predicting pearl millet production and water use based on weather data. The SWB was chosen because it is widely used in southern Africa; however, the developed parameters should benefit other models as well. The presented crop-specific parameter values were derived from field observations and literature. Varieties with different phenology, maturity dates and tillering habits were grown under well-watered and well-fertilised conditions for calibration purposes. The calibrated model was used to predict biomass production, grain yield and crop water use. The hybrid's water use efficiency was higher than that of the landrace and improved variety.

Indexed as

crop growthdry matter productionextinction coefficientmodellingpartitioningPennisetum glaucumradiation use efficiencySWB

Identifiers

PMID35336688
PMCPMC8951074

What OpenQuestion holds

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Registered trials

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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.