Skip to main content
Log in

SpatialTemporal Analysis to Investigate the Influence of in-Row Plant Spacing on the Sugarcane Yield

  • Research Article
  • Published:
Sugar Tech Aims and scope Submit manuscript

Abstract

Monitoring the spatial and temporal variability of plant populations is essential for the longevity of sugarcane fields and allows localized management and optimization of the use of agricultural resources on the farm. The objective of this study was to analyze the spatial and temporal relationship between sugarcane yield and plant spacing in regions with different yield potentials in commercial fields. For this, three-year harvest data were obtained in a commercial sugarcane field and the spacing between plants in the sugarcane row was measured using an RGB camera onboard an unmanned aerial vehicle (UAV). Spatial and temporal stability analysis in the sugarcane field was carried out. Regression analysis was used to describe the causal effect of plant spacing on yield for each year of study. As a result, the regions of the field were classified according their yield patterns, as high, low, stable and unstable. The spacing between plants among these regions varied and differently influenced the final yield. It was observed that plant density affects the sugarcane yield, considering regions of high and low production potential, and that the population density decreased over time as the stand deteriorated. Nonlinear modeling with logarithmic function (R2 = 0.83) was achieved, indicating that the yield grows gradually and more slowly as the spacing between plants increases when compared to linear models, as verified in other studies in the literature.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  • Amorim, F.R., M.T.O. Patino, and D.F.L. Santos. 2022. Soil tillage and sugarcane planting: an assessment of cost and economic viability. Scientia Agricola 79 (1): e20190317. https://doi.org/10.1590/1678-992X-2019-0317.

    Article  Google Scholar 

  • Van Antwerpen, R., P.D.R. van Heerden, M.G. Keeping, L.W. Titshall, A. Jumman, P.B. Tweddle, T. van Antwerpen, P.V. Ramouthar, and P.L. Campbell. 2022. A review of field management practices impacting root health in sugarcane. In Advances in Agronomy, ed. D.L. Sparks, 79–162. Cambridge: Academic Press. https://doi.org/10.1016/bs.agron.2022.02.002

  • Barbosa Júnior, M.R., D. Tedesco, R.G. Corrêa, B.R.A. Moreira, R.P. Silva, and C. Zerbato. 2021. Mapping gaps in sugarcane by UAV RGB imagery: the lower and earlier the flight, the more accurate. Agronomy 11 (12): 2578. https://doi.org/10.3390/agronomy11122578.

    Article  Google Scholar 

  • Belardo, G.C., M.T. Cassia, and R.P. Silva. 2015. Processos Agrícolas e Mecanização da Cana-de-Acúcar. Jaboticabal: SBEA.

  • Blackmore, S., R.J. Godwin, and S. Fountas. 2003. The analysis of spatial and temporal trends in yield map data over six years. Biosystems Engineering 84: 455–466. https://doi.org/10.1016/S1537-5110(03)00038-2.

    Article  Google Scholar 

  • Cambardella, C.A., T.B. Moorman, J.M. Novak, T.B. Parkin, D.L. Karlen, R.F. Turco, and A.E. Konopka. 1994. Field-scale variability of soil properties in Central Iowa soils. Soil Sciencie Society of America Journal 58: 1501–1511. https://doi.org/10.2136/sssaj1994.03615995005800050033x.

    Article  ADS  Google Scholar 

  • Casagrande, A.A. 1991. Tópicos de morfologia e fisiologia da cana-de-açúcar. Jaboticabal: FUNEP.

  • Chen, J., J. Wu, H. Qiang, B. Zhou, G. Xu, and Z. Wang. 2021. Sugarcane nodes identification algorithm based on sum of local pixel of minimum points of vertical projection function. Computers and Electronics in Agriculture 182: 105994. https://doi.org/10.1016/j.compag.2021.105994.

    Article  Google Scholar 

  • Companhia Nacional de Abastecimento (CONAB). 2023. Available online: https://www.conab.gov.br/info-agro/safras/cana Accessed 15 Apr 2023.

  • Ehsanullah, K.J., M. Jamil, and A. Ghafar. 2011. Optimizing the row spacing and seeding density to improve yield and quality of sugarcane. Crop & Environment 2 (1): 1–5.

    Google Scholar 

  • Ferreira, A.S., J. Marcato Junior, H. Pistori, F. Melgani, and W.N. Gonçalves. 2022. Unsupervised domain adaptation using transformers for sugarcane rows and gaps detection. Computers and Electronics in Agriculture 203: 107480. https://doi.org/10.1016/j.compag.2022.107480.

    Article  Google Scholar 

  • García, J.M., C. Molina, R. Simister, C.B. Taibo, L. Setten, L.E. Erazzú, L.D. Gómez, and A. Acevedo. 2023. Chemical and histological characterization of internodes of sugarcane and energy-cane hybrids throughout plant development. Industrial Crops and Products 199: 116739. https://doi.org/10.1016/j.indcrop.2023.116739.

    Article  CAS  Google Scholar 

  • Garilli, E., N. Bruno, F. Autelitano, R. Roncella, and F. Giuliani. 2021. Automatic detection of stone pavement’s pattern based on UAV photogrammetry. Automation in Construction 122: 103477. https://doi.org/10.1016/j.autcon.2020.103477.

    Article  Google Scholar 

  • Gasparotto, L.G., J.M. Rosa, and F.R. Marin. 2020. Interrow spacing and sugarcane yield in a diversity of climates: a major review. Agronomy Journal 112 (6): 4550–4557. https://doi.org/10.1002/agj2.20425.

    Article  Google Scholar 

  • Grami, A. 2023. Chapter 7 – Functions. In Discrete Mathematics, ed. A. Grami, 113–129. Cambridge: Academic Press. https://doi.org/10.1016/B978-0-12-820656-0.00007-1.

  • Jordahl, K., J. Van den Bossche, M. Fleischmann, J. Wasserman, J. McBride, J. Gerard, J. Tratner, M. Perry, A.G. Badaracco, C. Farmer, G.A. Hjelle, A.D. Snow, M. Cochran, S. Gillies, L. Culbertson, M. Bartos, N. Eubank, maxalbert, A. Bilogur, S. Rey, C. Ren, D. Arribas-Bel, L. Wasser, L.J. Wolf, M. Journois, J. Wilson, A. Greenhall, C. Holdgraf, Filipe, and F. Leblanc. 2020. geopandas/geopandas: v0.8.1. https://doi.org/10.5281/zenodo.3946761.

  • Kapur, R., S.K. Duttamajumder, and K. Krishna Rao. 2011. A breeder’s perspective on the tiller dynamics in sugarcane. Current Science 100 (2): 183–189.

    Google Scholar 

  • Kluyver, T., B. Ragan-Kelley, F. Pérez, B.E. Granger, M. Bussonnier, J. Frederic, ... and C. Willing. 2016. Jupyter Notebooks-a publishing format for reproducible computational workflows. Elpub 2016: 87–90.

  • Luna, I., and A. Lobo. 2016. Mapping crop planting quality in sugarcane from UAV imagery: a pilot study in Nicaragua. Remote Sensing 8 (6): 1–18. https://doi.org/10.3390/rs8060500.

    Article  Google Scholar 

  • Luz, F.B., L.C. Gonzaga, G.A.F. Castioni, R.P. Lima, J.L.N. Carvalho, and M.R. Cherubin. 2023. Controlled traffic farming maintains soil physical functionality in sugarcane fields. Geoderma 432: 116427. https://doi.org/10.1016/j.geoderma.2023.116427.

    Article  ADS  CAS  Google Scholar 

  • Maldaner, L.F., and J.P. Molin. 2020. Data processing within rows for sugarcane yield mapping. Scientia Agricola 77: e20180391. https://doi.org/10.1590/1678-992x-2018-0391.

    Article  Google Scholar 

  • Maldaner, L.F., J.P. Molin, T.F. Canata, and M. Martello. 2021a. A system for plant detection using sensor fusion approach based on machine learning model. Computers and Electronics in Agriculture 189: 106382. https://doi.org/10.1016/j.compag.2021.106382.

    Article  Google Scholar 

  • Maldaner, L.F., J.P. Molin, M. Martello, T.D. Tavares, and F.L.F. Dias. 2021b. Identification and measurement of gaps within sugarcane rows for site-specific management: comparing different sensor-based approaches. Biosystems Engineering 209: 64–73. https://doi.org/10.1016/j.biosystemseng.2021.06.016.

    Article  CAS  Google Scholar 

  • Maldaner, L.F., T.F. Canata, C.T.S. Dias, and J.P. Molin. 2021c. A statistical approach to static and dynamic tests for global navigation satellite systems receivers used in agricultural operations. Scientia Agricola 78 (5): e20190252. https://doi.org/10.1590/1678-992X-2019-0252.

    Article  Google Scholar 

  • Maldaner, L.F., T.F. Canata, and J.P. Molin. 2022. An approach to sugarcane yield estimation using sensors in the harvester and ZigBee technology. Sugar Tech 24: 813–821. https://doi.org/10.1007/s12355-021-01050-x.

    Article  CAS  Google Scholar 

  • Matsuoka, S., and R. Stolf. 2012. Sugarcane tillering and ratooning: Key factors for a profitable cropping. In Sugarcane: production, cultivation and uses, ed. J.F. Goncalves and K.D. Correia, 137–157. New York: Nova Science Publishers.

    Google Scholar 

  • Molin, J.P., L.R. Amaral, and A.F. Colaço. 2015. Agricultura de Precisão. São Paulo: Oficina de Textos.

  • Molin, J.P., and J.P.S. Veiga. 2016. Spatial variability of sugarcane row gaps: measurement and mapping. Ciência e Agrotecnologia 40 (3): 347–355. https://doi.org/10.1590/1413-70542016403046915.

    Article  Google Scholar 

  • Paula, V.R., and J.P. Molin. 2013. Assessing damage caused by accidental vehicle traffic on sugarcane ratoon. Applied Engineering in Agriculture. 29 (2): 161–169. https://doi.org/10.13031/2013.42642.

    Article  Google Scholar 

  • Peña-Villasenín, S., M. Gil-Docampo, and J. Ortiz-Sanz. 2020. Desktop vs cloud computing software for 3D measurement of building façades: the monastery of San Martín Pinario. Measurement 149: 106984. https://doi.org/10.1016/j.measurement.2019.106984.

    Article  Google Scholar 

  • Rocha, B.M., A.U. Fonseca, H. Pedrini, and F. Soares. 2023. Automatic detection and evaluation of sugarcane planting rows in aerial images. Information Processing in Agriculture 10 (3): 400–415. https://doi.org/10.1016/j.inpa.2022.04.003.

    Article  Google Scholar 

  • Rossi Neto, J., Z.M. Souza, S.R.M. Oliveira, O.T. Kölln, D.A. Ferreira, J.L.N. Carvalho, O.A. Braunbeck, and H.C.J. Franco. 2017. Use of the decision tree technique to estimate sugarcane productivity under edaphoclimatic conditions. Sugar Tech 19: 662–668. https://doi.org/10.1007/s12355-017-0509-7.

    Article  CAS  Google Scholar 

  • Rossi Neto, J., Z.M. Souza, O.T. Kölln, J.L.N. Carvalho, D.A. Ferreira, G.A.F. Castioni, L.C. Barbosa, S.G.Q. Castro, O.A. Braunbeck, A.L. Garside, and H.C.J. Franco. 2018. The arrangement and spacing of sugarcane planting influence root distribution and crop yield. Bioenergy Research 11: 291–304. https://doi.org/10.1007/s12155-018-9896-1.

    Article  Google Scholar 

  • Sales, C.R.G., R.V. Ribeiro, P.E.R. Marchiori, J. Kromdijk, and E.C. Machado. 2023. The negative impact of shade on photosynthetic efficiency in sugarcane may reflect a metabolic bottleneck. Environmental and Experimental Botany 211: 105351. https://doi.org/10.1016/j.envexpbot.2023.105351.

    Article  CAS  Google Scholar 

  • Santos, L.C.N., G.C.M. Teixeira, R.M. Prado, A.M.S. Rocha, and R.C.S. Pinto. 2020a. Response of pre-sprouted sugarcane seedlings to foliar spraying of potassium silicate, sodium and potassium silicate, nanosilica and monosilicic acid. Sugar Tech 22: 773–781. https://doi.org/10.1007/S12355-020-00833-Y.

    Article  Google Scholar 

  • Santos, L.S., N.C.C. Braga, T.M. Rodrigues, A. Rubio Neto, M.F. Brito, and E.C. Severiano. 2020b. Pre-sprouted seedlings of sugarcane using sugarcane industry by-products as substrate. Sugar Tech 22: 675–685. https://doi.org/10.1007/s12355-020-00798-y.

    Article  Google Scholar 

  • Santos, H.G., P.K.T. Jacomine, L.H.C. Anjos, V.A. Oliveira, J. F. Lumbreras, M.R. Coelho, J.A. Almeida, J.C. Araujo Filho, J.B. Oliveira, and T.J.F. Cunha. 2018. Sistema Brasileiro de Classificação de Solos. Brasília: Embrapa Solos.

  • Shirzadifar, A., M. Maharlooei, S.G. Bajwa, P.G. Oduor, and J.F. Nowatzki. 2020. Mapping crop stand count and planting uniformity using high resolution imagery in a maize crop. Biosystems Engineering 200: 377–390. https://doi.org/10.1016/j.biosystemseng.2020.10.013.

    Article  Google Scholar 

  • Stolf, R. 1986. Metodologia de avaliação de falhas nas linhas de cana-de-açúcar. STAB 4: 22–36.

    Google Scholar 

  • Stolf, R. 1989. Um modelo explicativo da competição entre colmos de um canavial e o vale da morte. STAB 8 (2): 27–34.

    Google Scholar 

  • Stolf, R., A.M. Iaia, and T.S.G. Lee. 1991. Índice de falhas segundo o método de Stolf: Correlação com o rendimento agrícola em soqueiras de cana-de-açúcar. Álcool e Açúcar 11 (58): 12–16.

    Google Scholar 

  • Stolf, R., T.B. Garcia, L.O. Neris, O. Trindade Junior, and K. Reichardt. 2016. Avaliação de Falhas em Cana-de-Açúcar segundo o método de Stolf utilizando Imagens Aéreas de Alta Precisão obtidas por Vant. STAB 34 (4): 32–39.

    Google Scholar 

  • Teixeira, G.C.M., R.M. Prado, A.M.S. Rocha, L.C.N. Santos, M.M.S. Sarah, P.L. Gratão, and C. Fernandes. 2020. Silicon in pre-sprouted sugarcane seedlings mitigates the effects of water deficit after transplanting. Journal of Soil Science and Plant Nutrition 20: 849–859. https://doi.org/10.1007/s42729-019-00170-4.

    Article  CAS  Google Scholar 

  • United Nations (UN). 2023. Available online: https://brasil.un.org/pt-br/sdgs Accessed 10 Apr 2023.

  • Whelan, B.M., and A.B. McBratney. 2000. The “null hypothesis” of precision agriculture management. Precision Agriculture 2: 265–279. https://doi.org/10.1023/A:1011838806489.

    Article  Google Scholar 

Download references

Acknowledgements

Authors acknowledge National Council for Scientific and Technological Development (CNPq) grant number 168643/2017-0, and also financial support promoted, at the beginning of the doctorate, by the Brazilian Federal Agency: Coordination for the Improvement of Higher Education Personnel (CAPES) – Finance Code 001, both for the first author; to the operational support of São Manoel sugarcane mill.

Author information

Authors and Affiliations

Authors

Contributions

LFM and JPM were involved in the conceptualization; LFM and JPM contributed to the methodology; LFM and EROS assisted in the formal analysis; LFM contributed to writing—original draft; LFM, JPM and EROS were involved in writing—review and editing; LFM, JPM and EROS contributed to the validation; JPM contributed to the supervision. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Eudocio Rafael Otavio da Silva.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Maldaner, L.F., Molin, J.P. & Otavio da Silva, E.R. SpatialTemporal Analysis to Investigate the Influence of in-Row Plant Spacing on the Sugarcane Yield. Sugar Tech 26, 194–206 (2024). https://doi.org/10.1007/s12355-023-01334-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12355-023-01334-4

Keywords

Navigation