Joseph Mung’atu
About
In The Last Decade
Joseph Mung’atu
44 papers receiving 338 citations
Peers
Comparison fields: 5 of 91
- Plant Science 179
- Soil Science 50
- Ecology, Evolution, Behavior and Systematics 42
- Insect Science 39
- Cell Biology 31
Countries citing papers authored by Joseph Mung’atu
This map shows the geographic impact of Joseph Mung’atu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Joseph Mung’atu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Joseph Mung’atu more than expected).
Fields of papers citing papers by Joseph Mung’atu
This network shows the impact of papers produced by Joseph Mung’atu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Joseph Mung’atu. The network helps show where Joseph Mung’atu may publish in the future.
Co-authorship network of co-authors of Joseph Mung’atu
This figure shows the co-authorship network connecting the top 25 collaborators of Joseph Mung’atu. A scholar is included among the top collaborators of Joseph Mung’atu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Joseph Mung’atu. Joseph Mung’atu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 6 | |
| 2 | 0 | |
| 3 | A Three-Step Nonparametric Estimation of Conditional Value-At-Risk Admitting a Location-Scale Model | 0 |
| 4 | Nonparametric Estimation of the Error Functional of a Location-Scale Model | 1 |
| 5 | Identifying Factors Influencing Selection of Banks by Customers in Rwanda: Principal Components Analysis Approach | 1 |
| 6 | 18 | |
| 7 | The Relationship Between Urbanization and Precipitation in Kisumu City: Co-Integration Analysis | 1 |
| 8 | Effect of Free Basic Education on Improving Quality of Life in Kenya: Evidence from Machakos County | 1 |
| 9 | Application of paired student t-test on impact of Anti-retroviral therapy on CD4 cell count among HIV Seroconverters in serodiscordant heterosexual relationships: A case study of Nyanza region, Kenya. | 3 |
| 10 | Estimation of Extreme Value at Risk in Rwanda Exchange Rate | 1 |
| 11 | Time series modeling of tourist accommodation demand in Kenya | 2 |
| 12 | The Retention Rates of Students In Public Secondary Schools Using The Cox Proportional Hazard Model: A Case of Kisumu County, Kenya | 0 |
| 13 | EFFECT OF INTEGRATED SOIL FERTILITY MANAGEMENT INTERVENTIONS ON THE ABUNDANCE AND DIVERSITY OF SOIL COLLEMBOLA IN EMBU AND TAITA DISTRICTS, KENYA | 7 |
| 14 | ABUNDANCE AND DIVERSITY OF SOIL MITES (ACARI) ALONG A GRADIENT OF LAND USE TYPES IN TAITA TAVETA, KENYA | 13 |
| 15 | COLLEMBOLA RICHNESS AND DIVERSITY ALONG A GRADIENT OF LAND-USE INTENSITY AND SOIL ABIOTIC FACTORS IN TAITA, KENYA | 0 |
| 16 | COMPARATIVE DESCRIPTION OF LAND USE AND CHARACTERISTICS OF BGBD BENCHMARK SITES IN KENYA | 10 |
| 17 | COLLEMBOLA RICHNESS AND DIVERSITY ALONG A LAND-USE INTENSITY GRADIENT IN TAITA, KENYA | 2 |
| 18 | MACROFAUNA DIVERSITY AND ABUNDANCE ACROSS DIFFERENT LAND USE SYSTEMS IN EMBU, KENYA | 26 |
| 19 | COLLEMBOLA DENSITY AND DIVERSITY ALONG A GRADIENT OF LAND- USE TYPES IN EMBU DISTRICT, EASTERN KENYA | 12 |
| 20 | INFLUENCE OF LAND USE TYPES ON OCCURRENCE OF ARBUSCULAR MYCORRHIZAL FUNGI IN THE HIGH ALTITUDE REGIONS OF MT. KENYA | 30 |
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.