Publication activity
Articles grouped by publication year
Hover over a year or focus the chart and use arrow keys to inspect values.
2026 is year to date. 0 undated publications excluded.
Research across participating journals · All insights follow your collection filters.
Publication activity alongside incoming citations.
Articles grouped by publication year
Hover over a year or focus the chart and use arrow keys to inspect values.
2026 is year to date. 0 undated publications excluded.
OpenAlex · year of the citing work
Hover over a year or focus the chart and use arrow keys to inspect values.
2017–2026 annual-history window. 2026 is year to date. Unknown years have gaps.
Leading affiliations and themes in this collection.
Full distributions & methodology ↓Independent sources. Clear time scales.
Crossref and OpenAlex · separate totals for the same publication cohorts
Hover over a year or focus the chart and use arrow keys to inspect values.
Latest lifetime totals, grouped by the cited article’s year. These are not citations received in that year.
OpenAlex · incoming citation links grouped by citing-work year
Hover over a year or focus the chart and use arrow keys to inspect values.
2017–2026 annual-history window. 2026 is year to date. Unknown years have gaps.
Select journals to compare the same collection filters. Source totals stay separate. These measures describe research activity, not journal quality.
Articles by publication year
Latest lifetime totals · grouped by publication year
Latest lifetime totals · grouped by publication year
By year of the citing work · 2017–2026
2026 is year to date. Crossref and OpenAlex lifetime charts share a citation scale. Unknown observations appear as gaps; known zero stays zero. Annual receipts use available OpenAlex histories and may not sum to lifetime totals. 0 undated publications excluded from publication-year charts.
Crossref citation coverage: 3 / 4 publications (75.0%). Latest observation: 21 Oct 2026, 07:22 UTC. Means and percentages use records with known counts.
DOI-matched OpenAlex observations for the same selected publications. These counts are separate from Crossref and are never added together.
Observation range: 21 Oct 2026 00:22 UTC — 21 Oct 2026 07:22 UTC. Missing matches are not zero citations. Previous observations may remain if a refresh fails or a DOI no longer matches.
OpenAlex affiliations and primary-topic classification. Each publication counts once per institution or country; multi-affiliated publications appear in multiple groups, so percentages can sum above 100%. Countries describe author affiliations, not nationality. Topics use one primary topic per publication.
4 / 4 with data · 0 missing or unmatched. Percentages use all 4 selected publications.
4 / 4 with data · 0 missing or unmatched. Percentages use all 4 selected publications.
4 / 4 with data · 0 missing or unmatched. Percentages use all 4 selected publications.
Citations are the latest recorded totals for articles published in each year, not citations received during that year. Crossref and OpenAlex remain separate and share the same citation scale. 2026 is incomplete; newer articles have had less time to receive citations.
0 undated publications excluded. Unknown citation counts are shown as —, never as zero. Partial coverage can understate totals; expand each table for known / total records.
| Publication year | Journal / scope | Publications | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2019 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2020 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2022 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2023 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2024 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2025 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2026 | All selected publications | 1 | 1 | 0/1 | 1/1 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2019 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| 2020 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2022 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2023 | All selected publications | 2 | 1 | 1/1 | 1/1 |
| 2024 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2025 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2026 | All selected publications | — | 1 | 0/1 | 1/1 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2019 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2020 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2021 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2022 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2023 | All selected publications | 4 | 1 | 1/1 | 1/1 |
| 2024 | All selected publications | 2 | 1 | 1/1 | 1/1 |
| 2025 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2026 | All selected publications | 0 | 1 | 0/1 | 1/1 |
Incoming citations to the selected publications, grouped by the year of the citing work. This is separate from lifetime citation totals grouped by the cited article’s publication year.
The publication filters above define which articles receive the citations. The citation-year range below only controls this chart and the incoming-citation comparison. Removing publication-year filters includes older articles’ incoming citations too.
Annual history available for 4 / 4 selected publications. Supported display window: 2017–2026; 2026 is incomplete. Within a successfully fetched ten-year window, omitted years mean zero. Missing histories and years beyond a saved observation’s window remain unknown (—); partial coverage can understate totals.
Annual-history observations (UTC): 2026-10-21T00:22:06.146393+00:00 — 2026-10-21T07:22:59.249100+00:00. Older observations may be retained after failed or incomplete refreshes.
| Citation year | Journal / scope | OpenAlex · Citations received by year | Publications | Annual history known |
|---|---|---|---|---|
| 2017 | All selected publications | 0 | 4 | 4/4 |
| 2018 | All selected publications | 0 | 4 | 4/4 |
| 2019 | All selected publications | 0 | 4 | 4/4 |
| 2020 | All selected publications | 0 | 4 | 4/4 |
| 2021 | All selected publications | 0 | 4 | 4/4 |
| 2022 | All selected publications | 0 | 4 | 4/4 |
| 2023 | All selected publications | 2 | 4 | 4/4 |
| 2024 | All selected publications | 2 | 4 | 4/4 |
| 2025 | All selected publications | 2 | 4 | 4/4 |
| 2026 | All selected publications | 1 | 4 | 4/4 |
Counts represent citation links, not unique citing papers across a journal: one paper citing two selected articles contributes two links. Older citations outside this window are excluded, so annual sums need not equal lifetime totals. OpenAlex methodology ↗
Publications by publication year. 2026 is an incomplete year.
| Period | Publications |
|---|---|
| 2019 | 1 |
| 2023 | 1 |
| 2024 | 1 |
| 2026 | 1 |
Latest Crossref totals for the selected publications; not citations received in the selected years.
Unknown counts are excluded from the mean, median and cited-publication rate.
Same filters, source and counting rules across journals. Descriptive statistics, not a quality ranking.
| Journal / publisher | Publications | Crossref citations | OpenAlex citations | OpenAlex coverage | Crossref mean | Crossref median | Crossref cited %* | Crossref coverage |
|---|---|---|---|---|---|---|---|---|
| International Journal of Finance & Banking Studies SSBFNET | 1 | — | 0 | 1/1 | — | — | — | 0/1 · 0.0% |
| International Journal of Research in Business and Social Science SSBFNET | 3 | 3 | 7 | 3/3 | 1.0 | 1 | 66.7 | 3/3 · 100.0% |
* Percentage among publications with a known citation count.
Recently, many developing nations are disproportionately experiencing the impact of shocks and stressors compared to the rest of the world due to increasing inadequate capability and capacity to withstand the shocks. In particular, farmers and farming households are faced with diverse repeated and unanticipated shocks, which may be socio-economic, ecological and/or environmental in nature. All these shocks become unbearable and worsen in terms of food insecurity. The welfare costs associated with the shocks are significant, attracting humanitarian and development policy experts’ attention to developing suitable interventions to build a resilient food system and society. Consequently, this study analyzed the correlates of farmers’ resilience to food insecurity in South-West Nigeria. Drawing on the data collected from 472 smallholder farmers randomly sampled, descriptive statistics, the United States Department of Agriculture (USDA)’s food insecurity experience scale approach, composite score technique, principal component analysis (PCA) and Structural equation modeling (SEM) were used to analyze the data. The findings indicated that more than half (55.3%) of the respondents were inadequate in almost all the resilience indicators and components, had low resilience capacity, and were vulnerable to food shocks and food insecurity. In comparison, only 7.8% have a high resilience capacity to be a buffer against food shocks. 54% of the respondents fall into a high food insecurity category, while only 1.9% fall into a very-low food insecurity continuum. The SEM analysis also revealed that social safety net (p<0.01), climate extreme events (p<0.01), access to essential services (p<0.01), presence of enabling institutional environment (p<0.01), and technical level (p<0.05) made direct impacts on the farmers’ food insecurity status. Conversely, possession of assets (p<0.01), and social capital/neighborhood effect (p<0.1) had inverse impacts on the farmers’ food insecurity status. The study recommended transparently implementing a social protection program to assist the farmer in building buffers against shocks. There is also a greater need for sustained investments across the resilience pillars and indicators to build farmers’ resilience capacity to food insecurity and other shocks.
Agriculture is the major source of livelihood and income for about 80% of the Zimbabwean population. However, in recent decades, climate change, political standoffs, hyperinflation, and an energy crisis have held the sector hostage. Policy and academic scholarship have mainly focused on reforming agricultural policies. Studies on the diagnostic potential of non-contributory social assistance in increasing resilience and recovery from shocks are limited. Focusing on south-eastern Zimbabwe, this article identifies climate change-related farming challenges. It explores how social assistance, cash transfers and material transfers, in particular, can generate the resilience of smallholder farmers to climate change. The article utilised mixed methods research data collected between June and December 2022. The study shows increased use of improved inputs, increased farm output and asset ownership, risk coping and non-farm investment, household income, food security, and production capabilities. The results provide a sufficient empirical rationale for promoting social assistance and smallholder agriculture interactions. It shows how social assistance interventions generate multiplier effects in smallholder farming. The government should normalise the increasing demand for smallholder farming products to promote agriculture through social protection.
This study aims to: (1) analyze the risk of production, price, and income of technical irrigated rice fields and rainfed rice field in South Lampung regency, 2) to analyze farmer behavior against rice farming risk on the technical irrigated rice field and rainfed rice field. The research was conducted in August - September 2016 in Kecamatan Palas, rice production center in South Lampung. The sample of farmers consist of 23 technical irrigated rice field farmers, and 37 rainfed rice field farmers, taken proportionally by random sampling method .Data were analyzed quantitatively including Coefficient of variation analysis, Bernoulli Theory, and Neuman Morgenstern The results show: (1) the risk of production, price, and income faced by rice farmers on technical irrigated rice field is lower than from rainfed; (2) most rice farmers behave neutrally to the risk, on technical irrigated rice fieldvalued78,26 percent and in rainfed rice field valued 81.08 percent. No farmers were found to be brave at risk either on technical irrigated rice fields or on the rain fed rice field.
The study was directed by the following specific objectives: (i) to examine the impact of the result-based approach on the performance of the National Agriculture Insurance Scheme, and (ii) to analyze the effect of the participatory approach on the performance of the scheme, and (iii) to investigate the role of the reflexive approach in enhancing the program’s outcomes. The theoretical framework drew on decision-making theory, agricultural insurance theory, and contingency theory. The target group for this study comprises roughly 1,463 people. From this group, a stratified sampling technique was been employed to select 313 respondents. Information for the research obtained via questionnaires, interviews, and document analysis, including monthly district reports. The instruments were pilot-tested, resulting in a Cronbach’s alpha of 0.94, which confirms strong reliability. Data analysis involved descriptive and inferential statistics, specifically correlation and regression at a 0.05 significance level, with the assistance of SPSS Version 28. Ethical standards was upheld by securing informed consent from participants and ensuring the confidentiality of all responses.
The study was directed by the following specific objectives: (i) to examine the impact of the result-based approach on the performance of the National Agriculture Insurance Scheme, and (ii) to analyze the effect of the participatory approach on the performance of the scheme, and (iii) to investigate the role of the reflexive approach in enhancing the program’s outcomes. The theoretical framework drew on decision-making theory, agricultural insurance theory, and contingency theory. The target group for this study comprises roughly 1,463 people. From this group, a stratified sampling technique was been employed to select 313 respondents. Information for the research obtained via questionnaires, interviews, and document analysis, including monthly district reports. The instruments were pilot-tested, resulting in a Cronbach’s alpha of 0.94, which confirms strong reliability. Data analysis involved descriptive and inferential statistics, specifically correlation and regression at a 0.05 significance level, with the assistance of SPSS Version 28. Ethical standards was upheld by securing informed consent from participants and ensuring the confidentiality of all responses.
Agriculture is the major source of livelihood and income for about 80% of the Zimbabwean population. However, in recent decades, climate change, political standoffs, hyperinflation, and an energy crisis have held the sector hostage. Policy and academic scholarship have mainly focused on reforming agricultural policies. Studies on the diagnostic potential of non-contributory social assistance in increasing resilience and recovery from shocks are limited. Focusing on south-eastern Zimbabwe, this article identifies climate change-related farming challenges. It explores how social assistance, cash transfers and material transfers, in particular, can generate the resilience of smallholder farmers to climate change. The article utilised mixed methods research data collected between June and December 2022. The study shows increased use of improved inputs, increased farm output and asset ownership, risk coping and non-farm investment, household income, food security, and production capabilities. The results provide a sufficient empirical rationale for promoting social assistance and smallholder agriculture interactions. It shows how social assistance interventions generate multiplier effects in smallholder farming. The government should normalise the increasing demand for smallholder farming products to promote agriculture through social protection.
Recently, many developing nations are disproportionately experiencing the impact of shocks and stressors compared to the rest of the world due to increasing inadequate capability and capacity to withstand the shocks. In particular, farmers and farming households are faced with diverse repeated and unanticipated shocks, which may be socio-economic, ecological and/or environmental in nature. All these shocks become unbearable and worsen in terms of food insecurity. The welfare costs associated with the shocks are significant, attracting humanitarian and development policy experts’ attention to developing suitable interventions to build a resilient food system and society. Consequently, this study analyzed the correlates of farmers’ resilience to food insecurity in South-West Nigeria. Drawing on the data collected from 472 smallholder farmers randomly sampled, descriptive statistics, the United States Department of Agriculture (USDA)’s food insecurity experience scale approach, composite score technique, principal component analysis (PCA) and Structural equation modeling (SEM) were used to analyze the data. The findings indicated that more than half (55.3%) of the respondents were inadequate in almost all the resilience indicators and components, had low resilience capacity, and were vulnerable to food shocks and food insecurity. In comparison, only 7.8% have a high resilience capacity to be a buffer against food shocks. 54% of the respondents fall into a high food insecurity category, while only 1.9% fall into a very-low food insecurity continuum. The SEM analysis also revealed that social safety net (p<0.01), climate extreme events (p<0.01), access to essential services (p<0.01), presence of enabling institutional environment (p<0.01), and technical level (p<0.05) made direct impacts on the farmers’ food insecurity status. Conversely, possession of assets (p<0.01), and social capital/neighborhood effect (p<0.1) had inverse impacts on the farmers’ food insecurity status. The study recommended transparently implementing a social protection program to assist the farmer in building buffers against shocks. There is also a greater need for sustained investments across the resilience pillars and indicators to build farmers’ resilience capacity to food insecurity and other shocks.
This study aims to: (1) analyze the risk of production, price, and income of technical irrigated rice fields and rainfed rice field in South Lampung regency, 2) to analyze farmer behavior against rice farming risk on the technical irrigated rice field and rainfed rice field. The research was conducted in August - September 2016 in Kecamatan Palas, rice production center in South Lampung. The sample of farmers consist of 23 technical irrigated rice field farmers, and 37 rainfed rice field farmers, taken proportionally by random sampling method .Data were analyzed quantitatively including Coefficient of variation analysis, Bernoulli Theory, and Neuman Morgenstern The results show: (1) the risk of production, price, and income faced by rice farmers on technical irrigated rice field is lower than from rainfed; (2) most rice farmers behave neutrally to the risk, on technical irrigated rice fieldvalued78,26 percent and in rainfed rice field valued 81.08 percent. No farmers were found to be brave at risk either on technical irrigated rice fields or on the rain fed rice field.
Source coverage refers to this result set; a publication may occur in several sources. Sync times refer to contributing journals.
Missing abstracts: 0 · Missing authors: 0 · Missing publication dates: 0 · Dates without day precision: 0
| Source | Most recent successful journal sync | Latest attempt |
|---|---|---|
| crossref | 21 Oct 2026 07:22 UTC · ijrbs | complete · ijrbs · 21 Oct 2026 07:22 UTC |
| ojs-rest | 21 Oct 2026 07:22 UTC · ijrbs | complete · ijrbs · 21 Oct 2026 06:46 UTC |
| oai-pmh | 20 Oct 2026 19:56 UTC · ijfbs | complete · ijfbs · 20 Oct 2026 19:55 UTC |
| openalex-enrichment | 21 Oct 2026 07:23 UTC · ijrbs | complete · ijrbs · 21 Oct 2026 07:22 UTC |