Abstract
• First multi-country, decade-long analysis of bean yield variability in LIFDCs. • Integrated meta-analysis and statistical modelling revealed key yield drivers. • Temporal yield stability contrasts with wide spatial variability among nations. • Climatic effects (rainfall and temperature) dominate bean yield variability. • Principal component analysis explained 96% of bean yield variability. • We provide data-driven guidance for resilience and productivity enhancement. This study examines the spatiotemporal variability of common bean ( Phaseolus vulgaris L.) yields and their drivers across 32 Low-Income Food-Deficit Countries (LIFDCs) from 2013 to 2022, addressing the limited understanding of how yield patterns change across regions and over time. This knowledge is vital for enhancing food and nutritional security in resource-limited settings. The research combined standardized yield data from FAOSTAT with information from published studies and national field trials to conduct a longitudinal meta-analysis. It integrates meta-analysis and modeling across countries to expand understanding of bean yield variability. Statistical methods, including One-Way ANOVA, Kruskal–Wallis tests, and Principal Component Analysis (PCA), were employed to evaluate yield variability and identify key influencing factors. The results showed that temporal variability remained relatively stable, with no significant differences in mean or median yields over the decade (p > 0. 05), while average yields varied within ±1.5 t ha -1 among countries. Spatial differences were more notable, with Mali showing the highest variability (mean = 10.03 t ha -1 , SD = 3.5 t ha -1 ), Eritrea the lowest (mean = 2.15 t ha -1 , SD = 0.5 t ha -1 ), and Tanzania displaying moderate variability (mean = 2.25 t ha -1 , SD = 1.1 t ha -1 ). Outliers, such as Mali’s higher yield values, likely reflect data aggregation from irrigated or experimental systems, requiring cautious interpretation. Rainfall and temperature emerged as the primary climatic factors influencing inter- annual fluctuations. PCA results indicated that broad temporal dynamics (PC 1) explained 96% of yield variability, while localized influences (PC 2) accounted for only 2%. ANOVA confirmed the absence of significant temporal trends, suggesting that long-term stability outweighs short-term variability. The findings suggest that strengthening climate resilience, inputs, and infrastructure enhances bean yield.
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Claims 9
Claim · model-written summary
Mean and median bean yields did not differ significantly across the decade.
- Taxon
- Phaseolus vulgaris L.
Phaseolus vulgarise1 - Outcome
- mean or median yieldse0
- Direction
- no significant differencese0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“temporal variability remained relatively stable, with no significant differences in mean or median yields over the decade (p > 0. 05)”
e1“common bean ( Phaseolus vulgaris L.) yields”
Claim · model-written summary
Average yields differed by ±1.5 t ha -1 among countries.
- Taxon
- Phaseolus vulgaris L.Phaseolus vulgarise1
- Outcome
- average yieldse0
- Value
- ±1.5 t ha -1e0
- Setting
- among countriese0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“average yields varied within ±1.5 t ha -1 among countries.”
e1“common bean ( Phaseolus vulgaris L.) yields”
Claim · model-written summary
Mali had the highest reported yield variability.
- Taxon
- Phaseolus vulgaris L.Phaseolus vulgarise1
- Outcome
- variabilitye0
- Direction
- higheste0
- Value
- SD = 3.5 t ha -1e0
- Qualifier
- mean = 10.03 t ha -1e0
- Setting
- Malie0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Mali showing the highest variability (mean = 10.03 t ha -1 , SD = 3.5 t ha -1 )”
e1“common bean ( Phaseolus vulgaris L.) yields”
Claim · model-written summary
Eritrea had the lowest reported yield variability.
- Taxon
- Phaseolus vulgaris L.Phaseolus vulgarise1
- Outcome
- loweste0
- Direction
- loweste0
- Value
- SD = 0.5 t ha -1e0
- Qualifier
- mean = 2.15 t ha -1e0
- Setting
- Eritreae0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Eritrea the lowest (mean = 2.15 t ha -1 , SD = 0.5 t ha -1 )”
e1“common bean ( Phaseolus vulgaris L.) yields”
Claim · model-written summary
Tanzania had moderate yield variability.
- Taxon
- Phaseolus vulgaris L.Phaseolus vulgarise1
- Outcome
- moderate variabilitye0
- Value
- SD = 1.1 t ha -1e0
- Qualifier
- mean = 2.25 t ha -1e0
- Setting
- Tanzaniae0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Tanzania displaying moderate variability (mean = 2.25 t ha -1 , SD = 1.1 t ha -1 )”
e1“common bean ( Phaseolus vulgaris L.) yields”
Claim · model-written summary
Rainfall and temperature were primary drivers of interannual fluctuations.
- Taxon
- Phaseolus vulgaris L.Phaseolus vulgarise1
- Outcome
- inter- annual fluctuationse0
- Setting
- Rainfall and temperaturee0
Not reportedDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Rainfall and temperature emerged as the primary climatic factors influencing inter- annual fluctuations.”
e1“common bean ( Phaseolus vulgaris L.) yields”
Claim · model-written summary
PC 1 explained 96% of yield variability.
- Taxon
- Phaseolus vulgaris L.Phaseolus vulgarise1
- Outcome
- yield variabilitye0
- Value
- 96%e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“broad temporal dynamics (PC 1) explained 96% of yield variability”
e1“common bean ( Phaseolus vulgaris L.) yields”
Claim · model-written summary
PC 2 accounted for 2% of yield variability.
- Taxon
- Phaseolus vulgaris L.Phaseolus vulgarise1
- Outcome
- yield variabilitye2
- Value
- 2%e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“localized influences (PC 2) accounted for only 2%.”
e1“common bean ( Phaseolus vulgaris L.) yields”
e2“yield variability, while localized influences (PC 2) accounted for only 2%”
Claim · model-written summary
The authors suggest strengthening climate resilience, inputs and infrastructure to enhance bean yield.
Verbatim
e0“The findings suggest that strengthening climate resilience, inputs, and infrastructure enhances bean yield.”