Common bean is one of the most important food legumes worldwide. However, low grain yields among smallholder farmers are often associated with insufficient mineral fertilization and difficulties in identifying nutrient deficiencies. The crop can establish symbiotic associations with nitrogen-fixing bacteria, meeting part of its nitrogen requirements through biological nitrogen fixation (BNF). We aimed to evaluate the effects of rhizobial inoculation and mineral nitrogen fertilization on the grain yield of common bean cultivars, as well as to assess the potential of the modified photochemical reflectance index (MPRI) as an indicator associated with grain yield. The experiment was conducted under field conditions in Lavras, Minas Gerais, Brazil. Seven common bean cultivars and four nitrogen sources were evaluated: inoculation with the UFLA 02-100 and CIAT 899 strains, mineral nitrogen fertilization, and a non-fertilized control. Agronomic and physiological traits were evaluated throughout the crop cycle, and aerial images were obtained at 50 days after sowing to determine MPRI. The cultivar × nitrogen source interaction was not significant for the evaluated traits. Inoculation with UFLA 02-100 resulted in grain yield comparable to mineral nitrogen fertilization despite lower shoot N accumulation, whereas CIAT 899 did not differ from the non-fertilized control in grain yield. MPRI was significantly associated with grain yield but not with shoot N accumulation, indicating its potential as a non-destructive indicator of crop performance.
Citation: Taine Teotonio T da Rocha, Vitório Antônio P de Souza, Adriano Teodoro Bruzi, Mateus Ribeiro Piza, Márcia Rufini, Fatima Maria de S Moreira, Paulo Henrique Frois C Barros, Antônio Henrique F de Carvalho, Júlia Silva P dos Santos, Raphael Rodrigues Pereira, Marcelo Araújo Junqueira Ferraz. Rhizobial inoculation and UAV-derived vegetation index associated with grain yield in common bean[J]. AIMS Agriculture and Food, 2026, 11(4): 1011-1025. doi: 10.3934/agrfood.2026050
Common bean is one of the most important food legumes worldwide. However, low grain yields among smallholder farmers are often associated with insufficient mineral fertilization and difficulties in identifying nutrient deficiencies. The crop can establish symbiotic associations with nitrogen-fixing bacteria, meeting part of its nitrogen requirements through biological nitrogen fixation (BNF). We aimed to evaluate the effects of rhizobial inoculation and mineral nitrogen fertilization on the grain yield of common bean cultivars, as well as to assess the potential of the modified photochemical reflectance index (MPRI) as an indicator associated with grain yield. The experiment was conducted under field conditions in Lavras, Minas Gerais, Brazil. Seven common bean cultivars and four nitrogen sources were evaluated: inoculation with the UFLA 02-100 and CIAT 899 strains, mineral nitrogen fertilization, and a non-fertilized control. Agronomic and physiological traits were evaluated throughout the crop cycle, and aerial images were obtained at 50 days after sowing to determine MPRI. The cultivar × nitrogen source interaction was not significant for the evaluated traits. Inoculation with UFLA 02-100 resulted in grain yield comparable to mineral nitrogen fertilization despite lower shoot N accumulation, whereas CIAT 899 did not differ from the non-fertilized control in grain yield. MPRI was significantly associated with grain yield but not with shoot N accumulation, indicating its potential as a non-destructive indicator of crop performance.
| [1] |
Uebersax MA, Cichy KA, Gomez FE, et al. (2022) Dry beans (Phaseolus vulgaris L.) as a vital component of sustainable agriculture and food security—A review. Legume Sci. https://doi.org/10.1002/leg3.155 |
| [2] |
Bender FR, Alves LC, da Silva JFM, et al. (2022) Microbiome of nodules and roots of soybean and common bean: Searching for differences associated with contrasting performances in symbiotic nitrogen fixation. Int J Mol Sci 23: 12035. https://doi.org/10.3390/ijms231912035 doi: 10.3390/ijms231912035
|
| [3] |
Nyawade SO, Karanja NN, Gachene CKK, et al. (2020) Optimizing soil nitrogen balance in a potato cropping system through legume intercropping. Nutr Cycl Agroecosys 117: 43–59. https://doi.org/10.1007/s10705-020-10054-0 doi: 10.1007/s10705-020-10054-0
|
| [4] |
Rocha TTT, Bruzi AT, Rufini M, et al. (2026) Additive and nonadditive genetic effects on biological nitrogen fixation in common bean. Agronomy 16: 857. https://doi.org/10.3390/agronomy16090857 doi: 10.3390/agronomy16090857
|
| [5] |
Vidican R, Mălinaș A, Ranta O, et al. (2023) Using remote sensing vegetation indices for the discrimination and monitoring of agricultural crops: A critical review. Agronomy 13: 3040. https://doi.org/10.3390/agronomy13123040 doi: 10.3390/agronomy13123040
|
| [6] |
Lipovac A, Bezdan A, Moravčević D, et al. (2022) Correlation between ground measurements and UAV sensed vegetation indices for yield prediction of common bean grown under different irrigation treatments and sowing periods. Water 14: 3786. https://doi.org/10.3390/water14223786 doi: 10.3390/water14223786
|
| [7] |
Boshkovski B, Tzerakis C, Doupis G, et al. (2021) Relationship between physiological and biochemical measurements with spectral reflectance for two Phaseolus vulgaris L. genotypes under multiple stress. Int J Remote Sens 42: 1230–1249. https://doi.org/10.1080/01431161.2020.1826061 doi: 10.1080/01431161.2020.1826061
|
| [8] |
Yang B, Wang M, Sha Z, et al. (2019) Evaluation of aboveground nitrogen content of winter wheat using digital imagery of unmanned aerial vehicles. Sensors 19: E4416. https://doi.org/10.3390/s19204416 doi: 10.3390/s19204416
|
| [9] |
Li D, Yang S, Du Z, et al. (2024) Application of unmanned aerial vehicle optical remote sensing in crop nitrogen diagnosis: A systematic literature review. Comput Electron Agr 227: 109565. https://doi.org/10.1016/j.compag.2024.109565 doi: 10.1016/j.compag.2024.109565
|
| [10] |
Peticilă A, Iliescu PG, Dinca L, et al. (2025) Vegetation indices from UAV imagery: Emerging tools for precision agriculture and forest management. AgriEngineering 7: 431. https://doi.org/10.3390/agriengineering7120431 doi: 10.3390/agriengineering7120431
|
| [11] |
Dantas AAA, Carvalho LG, Ferreira E (2007) Classificação e tendências climáticas em Lavras, MG. Cienc Agrotec 31: 1862–1866. https://doi.org/10.1590/S1413-70542007000600039 doi: 10.1590/S1413-70542007000600039
|
| [12] |
Martínez-Romero E, Segovia L, Mercante FM, et al. (1991) Rhizobium tropici, a novel species nodulating Phaseolus vulgaris L. beans and Leucaena sp. trees. Int J Syst Bacteriol 41: 417–426. https://doi.org/10.1099/00207713-41-3-417 doi: 10.1099/00207713-41-3-417
|
| [13] |
Soares ALL, Ferreira PAA, Pereira JPAR, et al. (2006) Eficiência agronômica de rizóbios selecionados e diversidade de populações nativas nodulíferas em Perdões (MG): Ⅱ - feijoeiro. Rev Bras Cienc Solo 30: 803–811. https://doi.org/10.1590/S0100-06832006000500006 doi: 10.1590/S0100-06832006000500006
|
| [14] |
Fred EB, Waksman SA (1928) Laboratory manual of general microbiology, McGraw-Hill Book Company. |
| [15] |
Arf O, Lemos LB, Soratto RP, et al. (2015) Aspectos gerais da cultura do feijão: Phaseolus vulgaris L. (V. 443). UNESP/FEPAF. |
| [16] |
Jones JB (1987) Nitrogen: Kjeldahl nitrogen determination—What's in a name. J Plant Nutr 10: 1675–1682. https://doi.org/10.1080/01904168709363706 doi: 10.1080/01904168709363706
|
| [17] |
França L (n. d.) LF Tools—Comprehensive solutions for cartographic production, surveying, drone mapping, GNSS data import, geospatial database management, digital image processing, and spatial analysis. Available from: https://plugins.qgis.org/plugins/lftools/. |
| [18] |
Yang C, Everitt JH, Murden D (2011) Evaluating high resolution SPOT 5 satellite imagery for crop identification. Comput Electron Agr 75: 347–354. https://doi.org/10.1016/j.compag.2010.12.012 doi: 10.1016/j.compag.2010.12.012
|
| [19] |
Kozak M, Piepho HP (2018) What's normal anyway? Residual plots are more telling than significance tests when checking ANOVA assumptions. J Agron Crop Sci 204: 86–98. https://doi.org/10.1111/jac.12220 doi: 10.1111/jac.12220
|
| [20] |
Bartlett MS (1937) Properties of sufficiency and statistical tests. P R Soc Lond A Math Phys Sci 160: 268–282. |
| [21] |
Akaike H (1974) A new look at the statistical model identification. IEEE T Automat Contr 19: 716–723. https://doi.org/10.1109/tac.1974.1100705 doi: 10.1109/tac.1974.1100705
|
| [22] |
Resende MDV, Duarte JB (2007) Precision and quality control in variety trials. Pesqui Agropecu Trop 37: 182–194. Available from: https://revistas.ufg.br/pat/article/view/1867. |
| [23] |
R Core Team (2024) R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria. |
| [24] |
Ferreira EB, Cavalcanti PP, Nogueira DA (2021) ExpDes. pt: Pacote experimental designs (Português) (R package version 1.2. 2). R package. https://doi.org/10.32614/CRAN.package.ExpDes.pt |
| [25] |
Wickham H, François R, Henry L, et al. (2023) dplyr: A grammar of data manipulation (R package version 1.1. 4). R package. https://doi.org/10.32614/CRAN.package.dplyr |
| [26] |
Wickham H, Vaughan D, Girlich M (2024) tidyr: Tidy messy data (R package version 1.3.1). R package. https://doi.org/10.32614/CRAN.package.tidyr |
| [27] |
Wickham H (2016) ggplot2: Elegant graphics for data analysis. Springer-Verlag New York. https://doi.org/10.1007/978-3-319-24277-4 |
| [28] |
Pervent M, Lambert I, Tauzin M, et al. (2021) Systemic control of nodule formation by plant nitrogen demand requires autoregulation-dependent and independent mechanisms. J Exp Bot 72: 7942–7956. https://doi.org/10.1093/jxb/erab374 doi: 10.1093/jxb/erab374
|
| [29] |
Valenzuela H (2024) Optimizing the nitrogen use efficiency in vegetable crops. Nitrogen 5: 106–143. https://doi.org/10.3390/nitrogen5010008 doi: 10.3390/nitrogen5010008
|
| [30] |
Casarin NB, Thiengo CC, Villalba Algarin CA, et al. (2025) Managing nitrogen sources in soybean– Rhizobium symbiosis during reproductive phenological stage: Partitioning symbiotic and supplemental N with 15N. Nitrogen 7: 1. https://doi.org/10.3390/nitrogen7010001 doi: 10.3390/nitrogen7010001
|
| [31] |
Siqueira JA, Marques DJ, Silva MCG, et al. (2023) The use of photosynthetic pigments and SPAD can help in the selection of bean genotypes under fertilization organic and mineral. Sci Rep 13: 22610. https://doi.org/10.1038/s41598-023-49582-4 doi: 10.1038/s41598-023-49582-4
|
| [32] |
Silva KF, Carvalho RH, Ferreira LVSF, et al. (2024) Evaluating the feasibility of late nodulation in common beans. Acta Sci-Agron 47: e69013. https://doi.org/10.4025/actasciagron.v47i1.69013 doi: 10.4025/actasciagron.v47i1.69013
|
| [33] |
Argaw A, Muleta D (2018) Effect of genotypes-Rhizobium-environment interaction on nodulation and productivity of common bean (Phaseolus vulgaris L.) in eastern Ethiopia. Environ Syst Res 6. https://doi.org/10.1186/s40068-017-0091-8 doi: 10.1186/s40068-017-0091-8
|
| [34] |
Kläsener GR, Ribeiro ND, Steckling SM, et al. (2022) Genetic divergence and selection of common bean cultivars based on plant architecture and grain yield. Rev Caatinga 35: 818–828. https://doi.org/10.1590/1983-21252022v35n409rc doi: 10.1590/1983-21252022v35n409rc
|
| [35] |
Silva CA, Abreu ÂFB, Ramalho MAP (2009) Plant architecture and grain yield in common bean progenies with erect and prostrate plant habit. Pesqui Agropecu Bras 44: 1647–1652. https://doi.org/10.1590/S0100-204X2009001200013 doi: 10.1590/S0100-204X2009001200013
|
| [36] |
Mwenda GM, Hill YJ, O'Hara GW, et al. (2023) Competition in the Phaseolus vulgaris-Rhizobium symbiosis and the role of resident soil rhizobia in determining the outcomes of inoculation. Plant Soil 487: 61–77. https://doi.org/10.1007/s11104-023-05903-0 doi: 10.1007/s11104-023-05903-0
|
| [37] |
Gunnabo AH, Geurts R, Wolde-meskel E, et al. (2020) Genetic interaction studies reveal superior performance of Rhizobium tropici CIAT899 on a range of diverse East African common bean (Phaseolus vulgaris L.) genotypes. Appl Environ Microb 86: e01763-19. https://doi.org/10.1128/AEM.01763-19 doi: 10.1128/AEM.01763-19
|
| [38] |
Duvnjak J, Katanic Z, Sarcevic H, et al. (2024) Analysis of the photosynthetic parameters, grain yield, and quality of different winter wheat varieties over a two-year period. Agronomy 14: 478. https://doi.org/10.3390/agronomy14030478 doi: 10.3390/agronomy14030478
|
| [39] |
Rorie RL, Purcell LC, Karcher DE, et al. (2011) The assessment of leaf nitrogen in corn from digital images. Crop Sci 51: 2174–2180. https://doi.org/10.2135/cropsci2010.12.0699 doi: 10.2135/cropsci2010.12.0699
|
| [40] |
Sun H, Feng M, Yang W, et al. (2022) Monitoring leaf nitrogen accumulation with optimized spectral index in winter wheat under different irrigation regimes. Front Plant Sci 13: 913240. https://doi.org/10.3389/fpls.2022.913240 doi: 10.3389/fpls.2022.913240
|
| [41] |
García-Martínez H, Flores-Magdaleno H, Khalil-Gardezi A, et al. (2020) Estimación de la fracción de cobertura de la vegetación en maíz (Zea mays) mediante imágenes digitales tomadas por un vehículo aéreo no tripulado (UAV). Rev Fitotec Mex 43: 399–409. https://doi.org/10.35196/rfm.2020.4.399 doi: 10.35196/rfm.2020.4.399
|
agrfood-11-04-050-supplementary.pdf |
![]() |
Daily precipitation and mean air temperature from February to May 2023 in Lavras, Minas Gerais, Brazil
Accumulated nitrogen at flowering (A); grain yield under different nitrogen sources (B); and common bean cultivars (C). Means are expressed in the original scale. Error bars represent the standard error (SE) of the mean. Means followed by different letters differ significantly according to Tukey's HSD test (P ≤ 0.05)
Canopy area and SPAD index under different nitrogen sources. (A, B) Observed means and quadratic regression fits with 95% confidence intervals. (C, D) Different letters indicate significant differences according to Tukey's HSD test (P ≤ 0.05); ns, not significant
Correlation matrix among agronomic traits and vegetation indices in common bean: Shoot dry matter (SDM), accumulated shoot nitrogen at flowering (AN), plant height (HT), canopy width (CW), canopy area (CA), modified photochemical reflectance index (MPRI), and grain yield (GY). *Significant at P ≤ 0.05 according to the t-test