Supporting operational site‐specific fertilization in rice cropping systems with infield smartphone measurements and Sentinel-2 observations

Abstract

Due to the low efficiency of nitrogen fertilizers in flooded rice paddies, there is a rising demand for tools able to detect crop nitrogen status in space and time to allow farmers to use the technical novelties of precision agriculture to improve fertilizer management in extensive fields. This work sets up an operational approach to increase nitrogen use efficiency of top-dressing fertilization by supporting variable rate fertilization in rice cropping systems. The procedure exploits (i) crop modelling to identify best periods for fertilization (When), (ii) Sentinel-2 imagery to draw management zones (MZ) and lead field scouting (Where), and (iii) smartphone app to measure nitrogen nutritional index (NNI) (How much). Automatically generated MZ from Sentinel-2 data were able to identify within field patches with different nutritional status and NNI data well described the crop temporal dynamic in relation to crop development and nutritional needs. The workflow was implemented to provide farmers with timely information on plant nutritional status during the 2018 growing season to define site-specific fertilization strategies implemented with variable rate technology (VRT). Tests conducted on 6 fields over 30 ha in 3 farms showed the feasibility of the proposed workflow in real farming conditions allowing a reduction of applied fertilizer up to 25% in the areas with sufficient nutritional status. Demonstration revealed that VRT based on geospatial information from integrated in-field and satellite data can provide agronomic and environmental benefits compared with standard fertilization resulting in promising outcomes both in terms of yield (increase in the range 0.2–0.5 t ha−1) and nitrogen use efficiency (increase up to 7.8%).

This is a preview of subscription content, access via your institution.

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

References

  1. Ata-Ul-Karim, S. T., Zhu, Y., Yao, X., & Cao, W. (2014). Determination of critical nitrogen dilution curve based on leaf area index in rice. Field Crops Research, 167, 76–85. https://doi.org/10.1016/j.fcr.2014.07.010.

    Article  Google Scholar 

  2. Berger, K., Verrelst, J., Féret, J.-B., Hank, T., Wocher, M., Mauser, W., et al. (2020). Retrieval of aboveground crop nitrogen content with a hybrid machine learning method. International Journal of Applied Earth Observation and Geoinformation, 92, 102174. https://doi.org/10.1016/j.jag.2020.102174.

    CAS  Article  Google Scholar 

  3. Berger, K., Verrelst, J., Féret, J.-B., Wang, Z., Wocher, M., Strathmann, M., et al. (2020). Crop nitrogen monitoring: Recent progress and principal developments in the context of imaging spectroscopy missions. Remote Sensing of Environment, 242, 111758. https://doi.org/10.1016/j.rse.2020.111758.

    Article  Google Scholar 

  4. Blondlot, A., Gate, P., & Poilvé, H. (2005). Providing operational nitrogen recommendations to farmers using satellite imagery. In J. V. Stafford (Ed.), 5th European conference on precision agriculture (pp. 345–352). Wageningen, The Netherlands: Wageningen Academic Publishers.

    Google Scholar 

  5. Busetto, L., Casteleyn, S., Granell, C., Pepe, M., Barbieri, M., Campos-Taberner, M., et al. (2017). Downstream services for rice crop monitoring in Europe: From regional to local scale. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(12), 5423–5441. https://doi.org/10.1109/JSTARS.2017.2679159.

    Article  Google Scholar 

  6. Bussay, A., Bassu, S., Ceglar, A., Cerrani, I., Fumagalli, D., Condado, G. S., et al. (2018, October). Crop monitoring in Europe. JRC MARS Bulletin, 26(10).

  7. Cao, Q., Miao, Y., Wang, H., Huang, S., Cheng, S., Khosla, R., et al. (2013). Non-destructive estimation of rice plant nitrogen status with Crop Circle multispectral active canopy sensor. Field Crops Research, 154, 133–144. https://doi.org/10.1016/j.fcr.2013.08.005.

    Article  Google Scholar 

  8. Cao, Y., Tian, Y., Yin, B., & Zhu, Z. (2013). Assessment of ammonia volatilization from paddy fields under crop management practices aimed to increase grain yield and N efficiency. Field Crops Research, 147, 23–31. https://doi.org/10.1016/j.fcr.2013.03.015.

    Article  Google Scholar 

  9. Cappelli, G., Pagani, V., Zanzi, A., Confalonieri, R., Romani, M., Feccia, S., et al. (2018). GLORIFY: A new forecasting system for rice grain quality in Northern Italy. European Journal of Agronomy, 97, 70–80. https://doi.org/10.1016/j.eja.2018.05.004.

    Article  Google Scholar 

  10. Casa, R., Pelosi, F., Pascucci, S., Fontana, F., Castaldi, F., Pignatti, S., et al. (2017). Early stage variable rate nitrogen fertilization of silage maize driven by multi-temporal clustering of archive satellite data. In J. A. Taylor, D. Cammarano, A. Prashar & A. Hamilton (Eds.), Proceedings of the 11th European Conference on Precision Agriculture. Advances in Animal Biosciences: Precision Agriculture (ECPA) 2017 (pp. 288–292). https://doi.org/10.1017/S2040470017000103.

  11. Chen, P., Haboudane, D., Tremblay, N., Wang, J., Vigneault, P., & Li, B. (2010). New spectral indicator assessing the efficiency of crop nitrogen treatment in corn and wheat. Remote Sensing of Environment, 114(9), 1987–1997. https://doi.org/10.1016/j.rse.2010.04.006.

    Article  Google Scholar 

  12. Chen, Q., Tian, Y., Yao, X., Cao, W., & Zhu, Y. (2014). Comparison of five nitrogen dressing methods to optimize rice growth. Plant Production Science, 17(1), 66–80. https://doi.org/10.1626/pps.17.66.

    Article  Google Scholar 

  13. Cilia, C., Panigada, C., Rossini, M., Meroni, M., Busetto, L., Amaducci, S., et al. (2014). Nitrogen status assessment for variable rate fertilization in maize through hyperspectral imagery. Remote Sensing, 6(7), 6549–6565. https://doi.org/10.3390/rs6076549.

    Article  Google Scholar 

  14. Confalonieri, R., Debellini, C., Pirondini, M., Possenti, P., Bergamini, L., Barlassina, G., et al. (2011). A new approach for determining rice critical nitrogen concentration. The Journal of Agricultural Science, 149(05), 633–638. https://doi.org/10.1017/S0021859611000177.

    Article  Google Scholar 

  15. Confalonieri, R., Foi, M., Casa, R., Aquaro, S., Tona, E., Peterle, M., et al. (2013). Development of an app for estimating leaf area index using a smartphone. Trueness and precision determination and comparison with other indirect methods. Computers and Electronics in Agriculture, 96, 67–74. https://doi.org/10.1016/j.compag.2013.04.019.

    Article  Google Scholar 

  16. Confalonieri, R., Acutis, M., Bellocchi, G., & Donatelli, M. (2009). Multi-metric evaluation of the models WARM, CropSyst, and WOFOST for rice. Ecological Modelling, 220, 1395–1410. https://doi.org/10.1016/j.ecolmodel.2009.02.017.

    Article  Google Scholar 

  17. Confalonieri, R., Gusberti, D., Bocchi, S., & Acutis, M. (2006). The CropSyst model to simulate the N balance of rice for alternative management. Agronomy for Sustainable Development, 26(4), 241–249. https://doi.org/10.1051/agro:2006022.

    CAS  Article  Google Scholar 

  18. Confalonieri, R., Paleari, L., Movedi, E., Pagani, V., Orlando, F., Foi, M., et al. (2015). Improving in vivo plant nitrogen content estimates from digital images: Trueness and precision of a new approach as compared to other methods and commercial devices. Biosystems Engineering, 135, 21–30. https://doi.org/10.1016/j.biosystemseng.2015.04.013.

    Article  Google Scholar 

  19. Confalonieri, R., Rosenmund, A. S., & Baruth, B. (2009). An improved model to simulate rice yield. Agronomy for Sustainable Development, 29(3), 463–474. https://doi.org/10.1051/agro/2009005.

    Article  Google Scholar 

  20. Delloye, C., Weiss, M., & Defourny, P. (2018). Retrieval of the canopy chlorophyll content from Sentinel-2 spectral bands to estimate nitrogen uptake in intensive winter wheat cropping systems. Remote Sensing of Environment, 216, 245–261. https://doi.org/10.1016/j.rse.2018.06.037.

    Article  Google Scholar 

  21. Féret, J.-B., Berger, K., De Boissieu, F., & Malenovský, Z. (2021). PROSPECT-PRO for estimating content of nitrogen-containing leaf proteins and other carbon-based constituents. Remote Sensing of Environment, 252, 112173. https://doi.org/10.1016/j.rse.2020.112173.

    Article  Google Scholar 

  22. Fitzgerald, G., Rodriguez, D., & O’Leary, G. (2010). Measuring and predicting canopy nitrogen nutrition in wheat using a spectral index—The canopy chlorophyll content index (CCCI). Field Crops Research, 116(3), 318–324. https://doi.org/10.1016/j.fcr.2010.01.010.

    Article  Google Scholar 

  23. Gitelson, A., & Merzlyak, M. N. (1994). Quantitative estimation of chlorophyll-a using reflectance spectra: Experiments with autumn chestnut and maple leaves. Journal of Photochemistry and Photobiology, B: Biology, 22(3), 247–252. https://doi.org/10.1016/1011-1344(93)06963-4.

    CAS  Article  Google Scholar 

  24. Goulding, K., Jarvis, S., & Whitmore, A. (2008). Optimizing nutrient management for farm systems. Philosophical Transactions of the Royal Society B: Biological Sciences, 363, 667–680. https://doi.org/10.1098/rstb.2007.2177.

    CAS  Article  Google Scholar 

  25. Hansen, S., Jensen, H. E., Nielsen, N. E., & Svendsen, H. (1991). Simulation of nitrogen dynamics and biomass production in winter wheat using the Danish simulation model DAISY. Fertilizer Research, 27(2–3), 245–259. https://doi.org/10.1007/BF01051131.

    CAS  Article  Google Scholar 

  26. Haque, K. M. S., Khaliq, Q. A., & Aktar, J. (2006). Effect of nitrogen on phenology, light interception and growth in aromatic rice. International Journal of Sustainable Crop Production, 1, 1–6.

    Google Scholar 

  27. Hartigan, J. A. (1985). The dip test of unimodality. The Annals of Statistics, 13, 70–84. https://doi.org/10.1214/aos/1176346577.

    Article  Google Scholar 

  28. Huang, S., Miao, Y., Zhao, G., Yuan, F., Ma, X., Tan, C., et al. (2015). Satellite remote sensing-based in-season diagnosis of rice nitrogen status in northeast China. Remote Sensing, 7(8), 10646–10667. https://doi.org/10.3390/rs70810646.

    Article  Google Scholar 

  29. Karcher, D. E., & Richardson, M. D. (2003). Quantifying turfgrass color using digital image analysis. Crop Science, 43, 943–951. https://doi.org/10.2135/cropsci2003.9430.

    Article  Google Scholar 

  30. Kefauver, S. C., Vincente, R., Vergara-Díaz, O., Melichar, J. P. E., Lopez, A., Araus, J. L., et al. (2017). Comparative UAV and field phenotyping to assess yield and nitrogen use efficiency in hybrid and conventional barley. Frontiers in Plant Science, 8, 1–15. https://doi.org/10.3389/fpls.2017.01733.

    Article  Google Scholar 

  31. Le Maire, G., François, C., & Dufrêne, E. (2004). Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements. Remote Sensing of Environment, 89(1), 1–28. https://doi.org/10.1016/j.rse.2003.09.004.

    Article  Google Scholar 

  32. Leesawatwong, M., Jamjod, S., Kuo, J., Dell, B., & Rerkasem, B. (2005). Nitrogen fertilizer increases seed protein and milling quality of rice. Cereal Chemistry, 82(5), 588–593. https://doi.org/10.1094/CC-82-0588.

    CAS  Article  Google Scholar 

  33. Lemaire, G., Jeuffroy, M. H., & Gastal, F. (2008). Diagnosis tool for plant and crop N status in vegetative stage. Theory and practices for crop N management. European Journal of Agronomy, 28(4), 614–624. https://doi.org/10.1016/j.eja.2008.01.005.

    CAS  Article  Google Scholar 

  34. Li, F., Miao, Y., Feng, G., Yuan, F., Yue, S., Gao, X., et al. (2014). Improving estimation of summer maize nitrogen status with red edge-based spectral vegetation indices. Field Crops Research, 157, 111–123. https://doi.org/10.1016/j.fcr.2013.12.018.

    Article  Google Scholar 

  35. Liu, X., Wang, H., Zhou, J., Hu, F., Zhu, D., Chen, Z., et al. (2016). Effect of N Fertilization pattern on rice yield, N Use efficiency and fertilizer-N Fate in the Yangtze River Basin, China. PLoS ONE, 11, 1–20. https://doi.org/10.1371/journal.pone.0166002.

    CAS  Article  Google Scholar 

  36. Long, D. H., Lee, F. N., & TeBeest, D. O. (2000). Effect of nitrogen fertilization on disease progress of rice blast on susceptible and resistant cultivars. Plant Disease, 84(4), 403–409. https://doi.org/10.1094/PDIS.2000.84.4.403.

    CAS  Article  Google Scholar 

  37. Majumdar, D., Kumar, S., Pathak, H., Jain, M. C., & Kumar, U. (2000). Reducing nitrous oxide emission from an irrigated rice field of North India with nitrification inhibitors. Agriculture, Ecosystems and Environment, 81(3), 163–169. https://doi.org/10.1016/S0167-8809(00)00156-0.

    CAS  Article  Google Scholar 

  38. Moreno-García, B., Casterad, M., Guillén, M., & Quílez, D. (2018). Agronomic and economic potential of vegetation indices for rice N recommendations under organic and mineral fertilization in Mediterranean regions. Remote Sensing, 10(12), 1908. https://doi.org/10.3390/rs10121908.

    Article  Google Scholar 

  39. Nutini, F., Confalonieri, R., Crema, A., Movedi, E., Paleari, L., Stavrakoudis, D., et al. (2018). An operational workflow to assess rice nutritional status based on satellite imagery and smartphone apps. Computers and Electronics in Agriculture, 154, 80–92. https://doi.org/10.1016/j.compag.2018.08.008.

    Article  Google Scholar 

  40. Pagani, V., Guarneri, T., Busetto, L., Ranghetti, L., Boschetti, M., Movedi, E., et al. (2019). A high-resolution, integrated system for rice yield forecasting at district level. Agricultural Systems, 168, 181–190. https://doi.org/10.1016/j.agsy.2018.05.007.

    Article  Google Scholar 

  41. Paleari, L., Movedi, E., Vesely, F., Thoelke, W., Tartarini, S., Foi, M., et al. (2019). Estimating crop nutritional status using smart apps to support nitrogen fertilization. A case study on paddy rice. Sensors (Basel, Switzerland), 19(4), 981. https://doi.org/10.3390/s19040981.

    CAS  Article  Google Scholar 

  42. Ranghetti, L., Boschetti, M., Nutini, F., & Busetto, L. (2020). “sen2r”: An R toolbox for automatically downloading and preprocessing Sentinel-2 satellite data. Computers and Geosciences, 139, 104473. https://doi.org/10.1016/j.cageo.2020.104473.

    Article  Google Scholar 

  43. Ranghetti, L., Cardarelli, E., Boschetti, M., Busetto, L., & Fasola, M. (2018). Assessment of water management changes in the Italian rice paddies from 2000 to 2016 using satellite data: A contribution to agro-ecological studies. Remote Sensing, 10(416), 1–23. https://doi.org/10.3390/rs10030416.

    Article  Google Scholar 

  44. Salette, J., & Lemaire, G. (1981). Sur la variation de la teneur en azote des graminées fourragères pendant leur croissance: Formulation d’une loi de diluition (On the variation of the nitrogen content of fodder grasses during their growth: Formulation of a dilution law). Compte Rendus de l’académie des Sciences deParis Série III, 292, 875–878.

    CAS  Google Scholar 

  45. Sharma, L. K., Bu, H., Denton, A., & Franzen, D. W. (2015). Active-optical sensors using red NDVI compared to red edge NDVI for prediction of corn grain yield in north Dakota, USA. Sensors, 15, 27832–27853. https://doi.org/10.3390/s151127832.

    Article  Google Scholar 

  46. Stroppiana, D., Boschetti, M., Brivio, P. A., & Bocchi, S. (2009). Plant nitrogen concentration in paddy rice from field canopy hyperspectral radiometry. Field Crops Research, 111(1–2), 119–129. https://doi.org/10.1016/j.fcr.2008.11.004.

    Article  Google Scholar 

  47. Stroppiana, D., Fava, F., Boschetti, M., & Brivio, P. A. (2019). Estimation of nitrogen content in herbaceous plants using hyperspectral vegetation indices. In E. P. S. Thenkabail, J. G. Lyon, & A. Huete (Eds.), Hyperspectral indices and image classifications for agriculture and vegetation (Vol. 2, pp. 201–225). Boca Raton, FL: CRC Press Taylor and Francis.

    Google Scholar 

  48. Tukey, J. W. (1977). Exploratory data analysis. Analysis, 2(1999), 688. https://doi.org/10.1007/978-1-4419-7976-6.

    Article  Google Scholar 

  49. Verrelst, J., Camps-Valls, G., Muñoz-Marí, J., Rivera, J. P., Veroustraete, F., Clevers, J. G. P. W., et al. (2015). Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties—A review. ISPRS Journal of Photogrammetry and Remote Sensing, 108, 273–290. https://doi.org/10.1016/j.isprsjprs.2015.05.005.

    Article  Google Scholar 

  50. Wang, L., Zhou, X., Zhu, X., & Guo, W. (2017). Estimation of leaf nitrogen concentration in wheat using the MK-SVR algorithm and satellite remote sensing data. Computers and Electronics in Agriculture, 140, 327–337. https://doi.org/10.1016/j.compag.2017.05.023.

    Article  Google Scholar 

  51. Wang, Y., Shi, P., Ji, R., Min, J., Shi, W., & Wang, D. (2020). Development of a model using the nitrogen nutrition index to estimate in-season rice nitrogen requirement. Field Crops Research, 245(October 2019), 107664. https://doi.org/10.1016/j.fcr.2019.107664.

    Article  Google Scholar 

  52. Xue, L., & Yang, L. (2008). Recommendations for nitrogen fertiliser topdressing rates in rice using canopy reflectance spectra. Biosystems Engineering, 100(4), 524–534. https://doi.org/10.1016/j.biosystemseng.2008.05.005.

    Article  Google Scholar 

  53. Zavattaro, L., Romani, M., Sacco, D., Bassanino, M., & Grignani, C. (2008). Fertilization management of paddy fields in piedmont (NW Italy). Italian Journal of Agronomy, 3, 201–212. https://doi.org/10.4081/ija.2008.201.

    Article  Google Scholar 

  54. Zhang, K., Yuan, Z., Yang, T., Lu, Z., Cao, Q., Tian, Y., et al. (2020). Chlorophyll meter–based nitrogen fertilizer optimization algorithm and nitrogen nutrition index for in-season fertilization of paddy rice. Agronomy Journal, 112(1), 288–300. https://doi.org/10.1002/agj2.20036.

    CAS  Article  Google Scholar 

  55. Zhang, W., Wu, L., Ding, Y., Yao, X., Wu, X., Weng, F., et al. (2017). Nitrogen fertilizer application affects lodging resistance by altering secondary cell wall synthesis in japonica rice (Oryza sativa). Journal of Plant Research, 130(5), 859–871. https://doi.org/10.1007/s10265-017-0943-3.

    CAS  Article  Google Scholar 

  56. Zhao, B., Duan, A., Ata-Ul-Karim, S. T., Liu, Z., Chen, Z., Gong, Z., et al. (2018). Exploring new spectral bands and vegetation indices for estimating nitrogen nutrition index of summer maize. European Journal of Agronomy, 93, 113–125. https://doi.org/10.1016/j.eja.2017.12.006.

    CAS  Article  Google Scholar 

Download references

Acknowledgements

This work is in thankful memory of Lorenzo Busetto, our beloved “il Lorenz”. A keen scientist, whose contribution and unforgettable friendship will always be remembered. The experimental activities were conducted in the Framework of SATURNO Project (progettosaturno.it) funded by Regione Lombardia FEASR -   PSR 2014–2020 Program (Programma di Sviluppo Rurale Misura 1 - Sottomisura 1.2. - Operazione 1.2.01). The authors are grateful to Carlo Franchino, Riccardo Braggio, Cassinetta farm and ISIDRO Team for hosting the experiment, carrying out field practices and supporting agronomic choices.

Author information

Affiliations

Authors

Corresponding authors

Correspondence to Francesco Nutini or Mirco Boschetti.

Additional information

Publisher’s note

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

Supplementary Information

Below is the link to the electronic supplementary material.

(DOCX 241 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Nutini, F., Confalonieri, R., Paleari, L. et al. Supporting operational site‐specific fertilization in rice cropping systems with infield smartphone measurements and Sentinel-2 observations. Precision Agric (2021). https://doi.org/10.1007/s11119-021-09784-0

Download citation

Keywords

  • Satellite monitoring
  • Nitrogen nutritional index
  • Variable rate fertilization
  • Digital agriculture