Evidence map›Paper›PMID 37113594›Full record

ArticleFrontiers in plant science2023

Exploring the potential of mapped soil properties, rhizobium inoculation, and phosphorus supplementation for predicting soybean yield in the savanna areas of Nigeria.

Martin Jemo, Krishna Prasad Devkota, Terence Epule Epule, Tarik Chfadi, Rkia Moutiq, Mohamed Hafidi, Francis B T Silatsa, Jibrin Mohamed Jibrin

Abstract read
In one paragraph

Article in Frontiers in plant science, 2023. 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

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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

8 authors.

Martin JemoAgroBiosciences Program, College for Sustainable Agriculture and Environmental Sciences, Mohammed VI Polytechnic University (UM6P), Bengeurir, Morocco.
Krishna Prasad DevkotaSoil, Water, and Agronomy (SWA) Program, International Center for Agricultural Research in the Dry Areas (ICARDA), Rabat-institute, Rabat, Morocco.
Terence Epule EpuleInternational Water Research Institute (IWRI), College for Sustainable Agriculture and Environmental Sciences, Mohammed VI Polytechnic University (UM6P), Bengeurir, Morocco.
Tarik ChfadiInternational Water Research Institute (IWRI), College for Sustainable Agriculture and Environmental Sciences, Mohammed VI Polytechnic University (UM6P), Bengeurir, Morocco.
Rkia MoutiqNational Institute of Agronomical Research (INRA), Regional Center of Kenitra, Kenitra, Morocco.
Mohamed HafidiAgroBiosciences Program, College for Sustainable Agriculture and Environmental Sciences, Mohammed VI Polytechnic University (UM6P), Bengeurir, Morocco.
Francis B T SilatsaCenter of Excellence for Soil and Fertilizer Research in Africa (CESFRA), College for Sustainable Agriculture and Environmental Sciences, Mohammed VI Polytechnic University (UM6P), Benguerir, Morocco.
Jibrin Mohamed JibrinCentre for Dryland Agriculture, Bayero University, Kano, Nigeria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rapid and accurate soybean yield prediction at an on-farm scale is important for ensuring sustainable yield increases and contributing to food security maintenance in Nigeria. We used multiple approaches to assess the benefits of rhizobium (Rh) inoculation and phosphorus (P) fertilization on soybean yield increase and profitability from large-scale conducted trials in the savanna areas of Nigeria [i.e., the Sudan Savanna (SS), Northern Guinea Savanna (NGS), and Southern Guinea Savanna (SGS)]. Soybean yield results from the established trials managed by farmers with four treatments (i.e., the control without inoculation and P fertilizer, Rh inoculation, P fertilizer, and Rh + P combination treatments) were predicted using mapped soil properties and weather variables in ensemble machine-learning techniques, specifically the conditional inference regression random forest (RF) model. Using the IMPACT model, scenario analyses were employed to simulate long-term adoption impacts on national soybean trade and currency. Our study found that yields of the Rh + P combination were consistently higher than the control in the three agroecological zones. Average yield increases were 128%, 111%, and 162% higher in the Rh + P combination compared to the control treatment in the SS, NGS, and SGS agroecological zones, respectively. The NGS agroecological zone showed a higher yield than SS and SGS. The highest training coefficient of determination (R

Indexed as

bradyrhizobium inoculationforesight IMPACT modelNigeria savanna agroecologiesparticipatory on-farm experimentrandom forest model

Identifiers

PMID37113594
PMCPMC10126304

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LicenceCC BY
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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.