臺灣博碩士論文加值系統

Sugarcane yield estimation is crucial for farmers, policymakers and other industries dependant on sugarcane. The purpose of this research is to enhance sugarcane productivity in Taiwan by integrating remote sensing technology, soil series analysis, and management practices. This study aims to identify the key soil series and vegetation indices (VIs) that better predict sugarcane yields, with a focus on soil pH and its impacts. The study was conducted in Yunlin, Taiwan, across six sugarcane farms managed by the Taiwan Sugar Company (TSC) from 2019 to 2022. Data on sugarcane productivity, remote sensing VIs, and soil characteristics were collected and analysed. Outliers influenced by other vegetation were removed using the three-sigma rule. Pearson correlation and multiple linear regression (MLR) analyses were used to evaluate the relationships between variables and sugarcane yield. Results showed significant yield variability across different farms, influenced by soil series and crop types. Autumn-planted crops (types A and J) consistently had higher yields compared to spring-planted crops. Soil series analysis revealed that farms with alkaline soils (Farm 56) had higher yields due to better nutrient availability and soil structure, whereas acidic soils (Farm 61) hindered growth due to nutrient unavailability and toxicity. Vegetation indices such as NDVI and SRVI positively correlated with yield, indicating that healthy and vigorous vegetation is crucial for optimal yield. Conversely, GNDVI showed a negative correlation, potentially reflecting increased plant stress factors such as pests, diseases, or water stress. Integrating remote sensing with soil and crop management can effectively predict and enhance sugarcane yields. The findings provide valuable insights for policymakers and farmers in Taiwan, emphasizing the importance of precise agricultural practices and tailored management strategies to improve productivity and sustainability in sugarcane farming.

Abstract i
Table of Contents ii
List of Tables iv
List of Figures v
Chapter 1: Introduction 1
1.1 Background Information 1
1.2 Research Gap 7
1.3 Research Goal and Objectives 8
Chapter 2: Literature Review 9
2.1 Remote Sensing Applications 9
2.2 Soil factors 11
2.3 Sugarcane management 14
2.4 Summary 15
Chapter 3: Materials and Methods 17
3.1 Study Area 17
3.2 Data Sets 19
3.3 Sentinel Data 20
3.4 Vegetation Indices 21
3.5 Soil Series 23
3.6 Data Analysis 23
3.6.1 Data screening 23
3.6.2 Pearson Correlation 24
3.6.3 Linear regression model 25
Chapter 4: Results and Discussions 26
4.1 Summary of Farm 54 26
4.1.1 Data Summary and Descriptive Statistics for Farm 54 26
4.1.2 Analysis of Variable Associations in Farm 54 28
4.1.3 Evaluation of Productive Models for Farm 54 31
4.2 Summary of Farm 56 32
4.2.1 Data Summary and Descriptive Statistics for Farm 56 32
4..2.2 Analysis of Variable Associations in Farm 56 34
4.2.3 Evaluation of Predictive Models for Farm 56. 36
4.3 Summary for Farm 58 37
4.3.1 Data Summary and Descriptive Statistics for Farm 58 37
4.3.2 Analysis of Variable Associations in Farm 58 39
4.3.3 Evaluation of Predictive Models for Farm 58 41
4.4 Summary for Farm 59 42
4.4.1 Data Summary and Descriptive Statistics for Farm 59 42
4.4.2 Analysis of Variable Associations in Farm 59 43
4.4.3 Evaluation of Predictive Models for Farm 59 46
4.5 Summary for Farm 61 47
4.5.1 Data Summary and Descriptive Statistics for Farm 61 47
4.5.2 Analysis of Variable Associations in Farm 61 49
4.5.3 Evaluation of Predictive Models for Farm 61. 51
4.6 Summary for Farm 62 52
4.6.1 Data Summary and Descriptive Statistics for Farm 62. 52
4.6.2 Analysis of Variable Associations in Farm 62 53
4.6.3 Evaluation of Predictive Models for Farm 62 54
4.7 Comparison of the Land Results 59
4.8 The Relationship of Sugarcane Yield and the Different Soil Series. 60
Chapter 5: Conclusion, Limitations and Implications 72
References 75
Appendices 81
Appendix A Soil Series Maps 81
Appendix B Soil pH 84

Amani, M., Mahdavi, S., & Berard, O. (2020). Supervised wetland classification using high spatial resolution optical, SAR, and LiDAR imagery. Journal of Applied Remote Sensing, 14(2), 024502. Retrieved from https://doi.org/10.1117/1.JRS.14.024502
Aspalter, C. (2001). The Taiwanese economic miracle: From sugarcane to high-technology. Understanding modern Taiwan: Essays in economics, politics and social policy, 1-32.
Awe, G. O., Reichert, J. M., & Fontanela, E. (2020). Sugarcane Production in the Subtropics: Seasonal Changes in Soil Properties and Crop Yield in No-Tillage, Inverting and Minimum Tillage. Soil and Tillage Research.
Bharadiya, J. P., Tzenios, N. T., & Reddy, M. (2023). Forecasting of crop yield using remote sensing data, agrarian factors and machine learning approaches. Journal of Engineering Research and Reports, 24(12), 29-44.
Bhatt, R. (2020). Resources management for sustainable sugarcane production. Resources use efficiency in agriculture, 647-693.
Bhatt, R., Majumder, D., Tiwari, A. K., Singh, S. R., Prasad, S., & Palanisamy, G. (2023). Climate-Smart Technologies for Improving Sugarcane Sustainability in India-A Review. Sugar Tech, 25(1), 1-14. doi:10.1007/s12355-022-01198-0
Cerri, D. G. P., & Magalhaes, P. S. G. (2012). Correlation of physical and chemical attributes of soil with sugarcane yield. Pesquisa Agropecuaria Brasileira, 613-620.
Chen, M. (2007). Sugar Industry in Taiwan. Taipei County: Walkers.
Chen, Z.-S., Hseu, Z.-Y., & Tsai, C.-C. (2015). The soils of Taiwan.
Da Silva, E. E., Baio, F. H. R., Teodoro, L. P. R., da Silva Junior, C. A., Borges, R. S., & Teodoro, P. E. (2020). UAV-multispectral and vegetation indices in soybean grain yield prediction based on in situ observation. Remote Sensing Applications: Society and Environment, 18, 100318.
de Oliveira, B. G., Lamparelli, R. A. C., & Dias, J. R. S. (2018). Estimating Sugarcane Yield Using Multi-Temporal Landsat Satellite Images and Machine Learning Algorithms. Remote Sensing.
Dias, H. B., & Sentelhas, P. C. (2018). Sugarcane yield gap analysis in Brazil - A multi-model approach for determinng magnitudes and causes. Science of the Total Environment, 1127-1136.
Dimov, D., Uhl, J. H., Löw, F., & Seboka, G. N. (2022). Sugarcane yield estimation through remote sensing time series and phenology metrics. Smart Agriclture Technology.
Dlamini, N. E., & Zhou, M. (2022). Soils and seasons effect on sugarcane ratoon yield. Field Crops Research, 284, 108588.
Dong, T., Liu, J., Qian, B., He, L., Liu, J., Wang, R., . . . Powers, J. (2020). Estimating crop biomass using leaf area index derived from Landsat 8 and Sentinel-2 data. ISPRS Journal of Photogrammetry and Remote Sensing, 168, 236-250.
Dos Santos, R. G., da Silva Junior, C. A., & Ferreira, L. G. (2020). Monitoring Sugarcane Yield Using Satellite Imagery and Regression Models. Remote Sensing Applications: Society and Environment.
FAO. (2017). Sugarcane. An Ancient Crop Seeking New Frontiers.
Fernandes, J. L., Ebecken, N. F. F., & Esquerdo, J. C. D. (2017). Sugarcane yield prediction in Brazil using NDVI time series and neural networks ensemble. International Journal of Remote Sensing, 38(16), 4631-4644. doi:10.1080/01431161.2017.1325531
Ferraro, D. O., Rivero, D. E., & Ghersa, C. M. (2009). An analysis of the factors that influence sugarcane yield in Northern Argentina using classification and regression trees. Field Crops Research, 112(2-3), 149-157. doi:10.1016/j.fcr.2009.02.014
Garcia, A. P., Umezu, C. K., Polania, E. C. M., Neto, A. F. D., Rossetto, R., & Albiero, D. (2022). Sensor-Based Technologies in Sugarcane Agriculture. Sugar Tech, 24(3), 679-698. doi:10.1007/s12355-022-01115-5
George, T. R., Bhat, J. A., Wani, M. A., Maqbool, M., Ramzan , S., & Yadav, R. (2021). Mapping of spatial variability of soil texture and micronutrients in Wangath watershed, Ganderbal District of Jammu and Kashir using GIS. Journal of Pharmacognosy and Phytochemistry, 638-642.
Hayashi, T. (2023). SugarSemi-annual (BR2023-0025). Retrieved from Brazil:
Jha, S. K., Patil, V. C., Rekha, B. U., Virnodkar, S. S., Bartalev, S. A., Plotnikov, D., . . . Patel, N. (2022). Sugarcane Yield Prediction Using Vegetation Indices in Northern Karnataka, India. Universal Journal of Agricultural Research.
Ji, Z., Pan, Y., Zhu, X., Wang, J., & Li, Q. (2021). Prediction of crop yield using phenological information extracted from remote sensing vegetation index. Sensors.
Kochain, L. V., Piñeros, M. A., & Liu, J. (2015). Plant Adaptation to Acid Soils: The Molecular Basis for Crop Aluminum Resistance. Annual Review of Plant Biology, 66, 571-598.
Kraeski, A., Almeida, F. T. d., Carvalho, T. M. d., & Souza, A. P. d. (2023). Identification of land use conflicts in Permanent Preservation Area in a Brazilian Amazon sub-basin. Sociedade & Natureza, 35, e65724.
Ku, C.-Y., & Liu, C.-Y. (2023). Modeling of land subsidence using GIS-based artificial neural network in Yunlin County, Taiwan. Scientific Reports, 13(1), 4090. doi:10.1038/s41598-023-31390-5
Kumar, B., Kamat, D. N., & Singh, S. P. (2019). Diversity Studies in Plant and Ratoon Crops for Selction of Profitable Sugarcane Genotypes Tolerant to Watelogging. International Journal of Current Microbiology and Applied Sciences, 8, 1925-1945.
Kumar, N., Singh, A. K., Kamat, D. N., Kumar, A., Minnatullah, M., Kumar, A., . . . Amitabh, A. (2023). Sugarcane Seed Production, Seed Standard and Seed Certification. Indian Institute of Suarcane Research.
Kusumawati, A., Satrio, B. F., & Kautsar, V. (2023). Determining of the Limiting Factors for Sugarcane (Saccharum officinarum) Productivity with Leaf Sampling Unit (LSU) Method in Sandy Soil. Earth and Environmental Sciences.
Liao, T. S. (2019). The Study of NDVI Unit Using One/ Dual Image Modules.
Liliane, T. N., & Charles, M. S. (2020). Factors affecting yield of crops. Agronomy-climate change & food security, 9.
Lisboa, I. P., Damian, M., Cherubin, M. R., Barros, P. P. S., Fiorio, P. R., Cerri, C. C., & Cerri, C. E. P. (2018). Prediction of Sugarcane Yield Based on NDVI and Concentration of Leaf-Tissue Nutrients in Fields Managed with Straw Removal. Agronomy-Basel, 8(9). doi:10.3390/agronomy8090196
Lofton, J., Tubana, B. S., Kanke, Y., Teboh, J., Viator , H., & Dalen, M. (2012). Estimating sugarcane yield potential using an in-season determination of normalixed difference vegetation index. Sensors, 7529-7547.
Marschner, P. (2012). In Marschner's Mineral Nutrition to Higher Plants (3 ed.): Academia Press.
Medar, R., & Rajpurohit, V. (2019). Sugarcane Crop Yield Forecasting Model Using Supervised Machine Learning. International Journal of Intelligent Systems and Applications, 8, 11-20.
Mishra, P., Al Khatib, A. M. G., Sardar, I., Mohammed, J., Karakaya, K., Dash, A., . . . Dubey, A. (2021). Modeling and forecasting of sugarcane production in India. Sugar Tech, 23(6), 1317-1324.
Mulianga, B., Bégué, B., Simoes, M., & Todoroff, P. (2013). Forecasting Regional Sugarcane Yield Based on Time Integral and Spatial Aggregation of MODIS NDVI. Remote Sensing. doi:10.3390/rs5052184
Munsif, F., Zahid, M., Arif, M., Ali, K., & Ahmad, I. (2018). Influence of Planting Date on Yield and Quality of Sugarcane under
the Agro-Climatic Conditions of Mardan. Sarhad Journal of Agriculture.
Nguyen, Q. C., Ngo, H. Y. T., & Vu, M. H. T. (2023). Advantages of Altering Cropping Schedules in the Face of Climate Varibility: A Case Study of Tan Ky Sugarcane Cultivation Area, Nghe An Province. Research on Crops, 24(1), 132-138.
Pacheco, L. P., & Guedes, M. G. (2017). Sugarcane Yield Prediction Using Remote Sensing Data. International Journal of Remote Sensing, 6649-6664.
Pandey, S., Patel, N. R., Danodia, A., & Singh, R. (2019). Discrimination of sugarcane and cane yiled estimation using Landsat and IRS resources at satellite data. The International Archives of the Photogremmetry, Remote Sensing and Spatial Information Sciences, 229-233.
Pang, Z., Tayyab, M., Kong, C., Liu, Q., Liu, Y., Hu, C., . . . Lin, W. (2021). Continuous sugarcane planting negatively impacts soil microbial community structure, soil fertility, and sugarcane agronomic parameters. Microorganisms, 9(10), 2008. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8537732/pdf/microorganisms-09-02008.pdf
Parker, R. (2022). Major Agronomic Crops - Sugar. In Plant and Soil Science: Fundamentals and Applications (2 ed., pp. 424-430): Cengage Learning.
Pukelsheim, F. (1994). The Three Sigma Rule. The American Statistician.
Puyu, V., Bakhmat, M., Khmelianchyshyn, Y., Stepanchenko, V., Bakhmat, O., & Pantsyreva, H. (2021). Social-and-ecological aspects of forage production reform in Ukraine in the early 21st century. European Journal of Sustainable Development, 10(1), 221-221.
Rabbi, S. F., Wilson, B. R., Lockwood, P. V., Daniel, H., & Young, I. M. (2014). Soil organic carbon mineralization rates in aggregates under contrasting land uses. Geoderma, 216, 10-18.
Rahman, M. M., & Robson, A. (2020). Integrating Landsat-8 and Sentinel-2 Time Series Data for Yield Prediction of Sugarcane Crops at the Block Level. Remote Sensing, 12(8). doi:10.3390/rs12081313
Ramburan, S., Wettergreen, T., Berry, S., & Shongwe, B. (2013). Genetic, environmental and management contributions to ratoon decline in sugarcane. Field Crops Research, 146, 105-112.
Rameshwar, R., & Lal, R. (2016). Soil Fertility Management for Sustainable Agriculture. In C. Press (Ed.).
Ramirez-Gi, J. G., Leon-Rueda, W. A., Castro-Franco, M., & Vargas, G. (2023). Population Dynamics and Estimation of Damage of the Spittlebug Aeneolamia varia on Sugarcane in Colombia by Using remote Sensing and Machine Learning Tools. Sugar Tech, 25(5), 1115-1133. doi:10.1007/s12355-023-01247-2
Rodrigues, M., Chauhan, T., & Rizvi, S. (2019). Sugarcane Yield Prediction Using Sentinel-2 Satellite imagery and Machine Learning Algorithms. International Journal of Remote Sensing.
Roumenina, E., Atzberger, C., Vassilev, V., Dimitrov, P., Kamenova, I., Banov, M., . . . Jelev, G. (2015). Single- and Multi-Date Crop Identification Using PROBA-V 100 and 300 m S1 Products on Zlatia Test Site, Bulgaria. Remote Sensing, 7(10), 13843-13862. doi:10.3390/rs71013843
Saini, P., Nagpal, B., Garg, P., & Kumar, S. (2023). Evaluation of Remote Sensing and Meteorological parameters for Yield Prediction of Sugarcane (Saccharum officinarum L.) Crop. Brazilian Archives of Biology and Technology, 66. doi:10.1590/1678-4324-2023220781
Samuels, P., & Gilchrist, M. (2014). Pearson Correlation.
Sanches, G. M., Megalhaes, P. S. G., & Franco, H. C. j. (2019). Site-specific Assessment of Spatial and Temporal Variability of Sugarcane Yield Related to Soil Attributes. Geoderma, 90-98.
Santana, D. C., de Oliveira Cunha, M. P., Dos Santos, R. G., Cotrim, M. F., Teodoro, L. P. R., da Silva Junior, C. A., . . . Teodoro, P. E. (2022). High-throughput phenotyping allows the selection of soybean genotypes for earliness and high grain yield. Plant Methods, 18(1), 13. Retrieved from https://plantmethods.biomedcentral.com/counter/pdf/10.1186/s13007-022-00848-4.pdf
Santos, D. P. D., Soares, A., de Medeiros, G., Christofoletti, D., Arantes, C. S., Vasconcelos, J. C. S., & Cancado, G. M. D. A. (2024). Evaluation of Sugarcane Yied Response to a Phosphate-Solubilizing Microbial Inoculant: Using an Aerial Imagery-Based Model. Sugar Tech, 26(1), 143-159.
Shiba, S. B., Mabaso, S. D., Dlamini, S. N., & Singwane, S. (2020). Remote Sensing for Sugarcane Crop Yield Estimation in Eswatini. Case of Lower Usuthu Smallholder Irrigation Project Sugarcane Farms. International Journal of Agriculture, Forestry and Fisheries., 19 -27.
Silva, T. S. F., Walford, N. S., & Wardlow, B. D. (2019). Assessing Sugarcane Yield Using Time-Series MODIS EVI Data. Remote Sensing.
Singels, A., Jackson, P., & Inman-Bamber, G. (2021). Sugarcane. Crop Physiology Case Histories for Major Crops, 674-713.
Singh, D. K., Kumar, S., Kumar, P., & Rathi, A. S. (2015). SPRING SUGARCANE: A PROMISING CROP in subtropical India. Indian Farming, 60(1).
Singh, R., Kumar, S., Kumar, A., & Singh, S. (2019). Role of Calcium in Plant Growth and Development. 33-52.
Som-ard, J., Immitzer, M., Vuolo, F., & Atzberger, C. (2024). Sugarcane yield estimation in Thailand at multiple scales
using the integration of UAV and Sentinel‑2 imagery. Precision Agriculture.
Sridhara, S., Soumya, B. R., & Kashayap, G. R. (2024). Multistage Sugarcane Yeld Prediction Using Machine Learning Algorithms. Journal of Agrometeorology, 26(1), 37-44. Retrieved from https://doi.org/10.54386/jam.v26il.2411
Srinivasarao, C., Kundu, S., Lakshmi, C. S., Rani, Y. S., Nataraj, K., Gangaiah, B., . . . Nagalakshm, S. (2019). Soil health issues for sustainability of South Asian Agriculture.
Tfwala, C. M., Dlamini, M. E., Mndzawe, D. M., Ndlangamandla, N., & Malindzisa, N. (2022). Sugarcane Yield Estimation Using Key Weather Parameters at the Ubombo Sugar Estate, Eswatini. 38-42.
Thapa, R. B., Dale, P., & Malano, H. (2018). Sugarcane Yield Prediction Using Time-Series MODIS Data: A case Study in Tully, Australia. Computers and Electronics in Agriculture.
Todd, J., & Johnson, R. (2021). Prediction of Ratoon Sugarcane Family Yield and Selection Using Remote Imagery. Agronomy-Basel, 11(7). doi:10.3390/agronomy11071273
USGS. (no date). What is Remote Sening and What is it Used for? United States
Van Antwerpen, R., van Heerden, P., Keeping, M., Titshall, L., Jumman, A., Tweddle, P., . . . Campbell, P. (2022). A review of field management practices impacting root health in sugarcane. Advances in Agronomy, 173, 79-162.
Vasconcelos, J. C. S., Speranza, E. A., Antunes, J. F. G., Barbosa, L. A. F., Christofoletti, D., Severino, F. J., & de Almeida Cancado, G. M. (2023). Development and Validation of a Model Based on Vegetation Indices for The Prediction of Sugarcane Yield. AgriEngineering, 5, 698-719.
Verma, A. K., & Raju, P. L. N. (2019). Remote Sensing-Based Sugarcane Yield Estimation: A Review. Sugar Tech.
Viswanathan, R. (2024). Degeneration in Sugarcane Varieties: Does the Sugar Industry Realize it? Sugar Tech, 1-4.
Wakgari, T., Kibret, K., Bedadi, B., Temesgen, M., & Erkossa, T. (2020). Effects of long term sugarcane production on soils physicochemical properties at Finchaa sugar Estate. Journal of Soil Science and Environmental Management, 11(1), 30-40.
Wan, L., Cen, H., Zhu, J., Zhang, J., Zhu, Y., Sun, D., . . . Li, Y. (2020). Grain yield prediction of rice using multi-temporal UAV-based RGB and multispectral images and model transfer–a case study of small farmlands in the South of China. Agricultural and Forest Meteorology, 291, 108096.
Wang, J., Zhang, J., Bai, Y., Zhang, S., Yang, S., & Yao, F. (2020). Integrating remote sensing-based process model with environmental zonation scheme to estimate rice yield gap in Northeast China. Field Crops Research, 246, 107682.
Wang, Z. W., Lu, Y. S., Zhao, G. P., Sun, C. L., Zhang, F. H., & He, S. (2022). Sugarcane Biomass Prediction with Multi-Mode Remote Sensing Data Using Deep Archetypal Analysis and Integrated Learning. Remote Sensing, 14(19). doi:ARTN 4944
10.3390/rs14194944
Weil, R. R., & Brady, N. C. (2017). The Nature and Properties of Soils. In (15 ed.): Pearson Education.
Wu, B., Zhang, M., Zeng, H., Tian, F., Potgieter, A. B., Qin, X., . . . Dong, Q. (2023). Challenges and opportunities in remote sensing-based crop monitoring: A review. National Science Review, 10(4), nwac290.
Wu, Q. H., Zhang, S. X., Feng, G., Zhu, P., Huang, S. M., Wang, B. R., & Xu, M. G. (2020). Determining the optimum range of soil Olsen P for high P use efficiency, crop yield, and soil fertility in three typical cropland soils. Pedosphere, 30(6), 832-843. doi:10.1016/s1002-0160(20)60040-6
Xu, J. X., Ma, J., Tang, Y. N., Wu, W. X., Shao, J. H., Wu, W. B., . . . Guo, H. Q. (2020). Estimation of Sugarcane Yield Using a Machine Learning Approach Based on UAV-LiDAR Data. Remote Sensing, 12(17). doi:10.3390/rs12172823
Yuan, J., Lv, X., & Li, R. (2018). A Speckle Filtering Method Based on Hypothesis Testing for Time-Series SAR Images. Remote Sensing.
Zhong, X., Hu, L., Huete, A. R., Zhang, X., & Li, Y. (2017). Satelite remote sensing reveals reduced productivity linked to land degradationin global drylands. Nature plants, 3(9).