Effects of Spacing and Post-Pinching Regrowth on Biomass Allocation, Root–Shoot Ratio, and Canopy Development of Moringa Species in Semi-Arid Northern Nigeria

Post-pinching regrowth and plant spacing are key determinants of vegetative performance, biomass allocation, and canopy development in Moringa species under semi-arid conditions. A factorial experiment was conducted in a Randomized Complete Block Design with three replicates to evaluate the effects of four spacing treatments (15×15, 15×20, 20×20, 20×30 cm) on the growth performance, root–shoot ratio, and leaf area index (LAI) of Moringa peregrina, M. oleifera, M. stenopetala, and M. oleifera ‘PKM 1’. Seedlings were pinched at 4 weeks after emergence to stimulate branching, and growth parameters were measured over 8 weeks. Post-pinching regrowth significantly influenced the number of leaves (NL) and branches (NB), with M. oleifera ‘PKM 1’ showing superior performance (NL = 261 ± 10.5; NB = 18 ± 1.5). Closer spacing enhanced NL, NB, and plant height, whereas wider spacing promoted root allocation and structural development. Root–shoot ratio decreased with increasing spacing, indicating adaptive biomass partitioning to optimize resource acquisition, while LAI varied among species, reflecting differences in canopy architecture and light interception efficiency. Regression analyses revealed strong positive relationships between root and shoot biomass (R² = 0.85–0.94) and negative correlations between R/S ratio and spacing (R² = 0.80–0.89), demonstrating species-specific plasticity in growth strategies. These findings highlight the importance of optimizing spacing and post-pinching management to enhance leaf yield, biomass allocation, and productivity in Moringa agroforestry systems, providing actionable insights for sustainable and resilient cultivation in semi-arid environments.

Moringa species, post-pinching regrowth, spacing, biomass allocation, root–shoot ratio, leaf area index, semi-arid agroforestry

1. Aliyu, M., and Odulaja, A. (2020). Climate variability and semi-arid agriculture in Sokoto State, Nigeria. Nigerian Journal of Agricultural Research, 15(1), 33–45. [Google Scholar] [Crossref]

2. Ejidike, C., Bello, H., and Musa, A. (2021). Agroclimatic characterization of the Sudano–Sahelian zone of Nigeria: Implications for dryland cropping systems. International Journal of Climate Change Strategies and Management, 13(3), 347–363. https://doi.org/10.1108/IJCCSM-09-2020-0098 [Google Scholar] [Crossref]

3. Eshete, Z., Belay, M., and Tadesse, F. (2022). Effects of plant spacing on growth, leaf yield, and biomass allocation of Moringa species in semi-arid Ethiopia. Heliyon, 8(4), e09245. https://doi.org/10.1016/j.heliyon.2022.e09245 [Google Scholar] [Crossref]

4. Garba, S., Muhammad, T., and Ali, K. (2022). Soil fertility constraints and nutrient management strategies in northern Nigerian semi-arid regions. African Journal of Soil Science, 12(1), 25–37. [Google Scholar] [Crossref]

5. Li, F., Zhang, Y., and Wang, X. (2022). Plant density effects on biomass allocation and canopy structure in fast-growing agroforestry species. Forests, 13(5), 678. https://doi.org/10.3390/f13050678 [Google Scholar] [Crossref]

6. Muhammad, A., and Bello, S. (2024). Soil and vegetation dynamics in semi-arid agroecosystems of Sokoto State, Nigeria. Journal of Environmental and Agricultural Sciences, 17(1), 12–26. [Google Scholar] [Crossref]

7. Namesi, T., Ibrahim, J., and Abubakar, S. (2010). Effects of NPK fertilizer on early growth of Moringa oleifera seedlings under semi-arid conditions. Nigerian Journal of Horticultural Science, 15(2), 45–52. [Google Scholar] [Crossref]

8. Ogbuehi, C., and Adepoju, T. (2020). Post-coppicing regrowth and biomass allocation in woody perennials of semi-arid Nigeria. Agroforestry Systems, 94(3), 675–688. https://doi.org/10.1007/s10457-019-00454-2 [Google Scholar] [Crossref]

9. Olayanju, O., Suleiman, A., and Muhammad, F. (2022). Improved Moringa varieties and their vegetative regrowth dynamics under defoliation stress. Journal of Agroforestry Research, 19(1), 55–68. [Google Scholar] [Crossref]

10. Patricio, A., Santos, R., and Pereira, M. (2017). Effects of spacing and harvest interval on growth and leaf yield of Moringa oleifera. Journal of Agricultural Science, 9(4), 122–134. https://doi.org/10.5539/jas.v9n4p122 [Google Scholar] [Crossref]

11. Said, A., Ali, M., and Bello, H. (2023). Leaf area index and biomass production responses of Moringa species to post-pinching regrowth in semi-arid agroforestry systems. Agroforestry Systems, 97(1), 45–60. https://doi.org/10.1007/s10457-022-00789-3 [Google Scholar] [Crossref]

12. Santos, R., Oliveira, P., and Silva, J. (2021). Influence of plant density on leaf yield and biomass allocation in Moringa stenopetala. Forest Ecology and Management, 485, 118961. https://doi.org/10.1016/j.foreco.2021.118961 [Google Scholar] [Crossref]

13. Soomro, F., Qureshi, A., and Memon, M. (2024). Plant density and leaf production optimization in fast-growing tropical trees. Journal of Tropical Agriculture, 62(2), 150–163. [Google Scholar] [Crossref]

14. Sutarno, H., and Rosyida, F. (2020). Effects of planting density on biomass production and light interception in Moringa plantations. Indonesian Journal of Agricultural Science, 21(3), 89–97. https://doi.org/10.21082/ijas.v21n3.2020.p89-97 [Google Scholar] [Crossref]

15. Zhang, L., Chen, Y., and Wang, Q. (2021). Biomass allocation strategies of woody species under competition and variable spacing. Plant Ecology, 222, 987–1000. https://doi.org/10.1007/s11258-021-01132-x [Google Scholar] [Crossref]

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