MONITOREO DEL RENDIMIENTO DE SOJA A PARTIR DE SENSORES REMOTOS
Palavras-chave:
radiación absorbida, sensores remotos, cultivos de soja, eficiencia de conversiónResumo
Este trabajo busca generar modelos que permitan pronosticar el rendimiento del cultivo de soja previo a la cosecha. Se explorará el impacto de factores meteorológicos en la variabilidad de la Eficiencia de Conversión (EC). Se analizará el aporte de la Radiación Fotosintéticamente Activa Absorbida (APAR) y la EC en la predicción de estos modelos, y cómo influye la fenología en dicho proceso. Además, se evaluará la capacidad y alcance de pronóstico de estos modelos antes de la cosecha.
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Amani, S., Shafizadeh-Moghadam, H. 2023. A review of machine learning models and influential factors for estimating evapotranspiration using remote sensing and ground-based data. Agric. Water Manag. 284, 108324. https://doi.org/10.1016/j.agwat.2023.108324
Darra, N., Anastasiou, E., Kriezi, O., Lazarou, E., Kalivas, D., Fountas, S. 2023. Can Yield Prediction Be Fully Digitilized? A Systematic Review. Agronomy 13, 2441. https://doi.org/10.3390/agronomy13092441
FAO. 1986. Early agrometeorological crop yield assessment, FAO plant production and protection paper. Food and Agriculture Organization of the United Nations, Rome.
Fernández-Long, M.E., Murphy, G., Spescha, L. 2012. Modelo de Balance Hidrológico Operativo para el Agro (BHOA). Rev Agron. Ambiente 32 1–2.
Fritz, S., See, L., Bayas, J.C.L., Waldner, F., Jacques, D., Becker-Reshef, I., Whitcraft, A., Baruth, B., Bonifacio, R., Crutchfield, J., Rembold, F., Rojas, O., Schucknecht, A., Van Der Velde, M., Verdin, J., Wu, B., Yan, N., You, L., Gilliams, S., Mücher, S., Tetrault, R., Moorthy, I., McCallum, I. 2019. A comparison of global agricultural monitoring systems and current gaps. Agric. Syst. 168, 258–272. https://doi.org/10.1016/j.agsy.2018.05.010
Gaso, D.V., De Wit, A., Berger, A.G., Kooistra, L. 2021. Predicting within-field soybean yield variability by coupling Sentinel-2 leaf area index with a crop growth model. Agric. For. Meteorol. 308–309, 108553. https://doi.org/10.1016/j.agrformet.2021.108553
Gitelson, A.A. 2004. Wide Dynamic Range Vegetation Index for Remote Quantification of Biophysical Characteristics of Vegetation. J. Plant Physiol. 161, 165–173. https://doi.org/10.1078/0176-1617-01176
Gitelson, A.A., Peng, Y., Huemmrich, K.F. 2014. Relationship between fraction of radiation absorbed by photosynthesizing maize and soybean canopies and NDVI from remotely sensed data taken at close range and from MODIS 250m resolution data. Remote Sens. Environ. 147, 108–120. https://doi.org/10.1016/j.rse.2014.02.014
Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X., Ferreira, L.G. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 83, 195–213. https://doi.org/10.1016/S0034-4257(02)00096-2
Jones, C.A., Kiniry, J.R., Dyke, P.T. (Eds.) 1986. CERESMaize: a simulation model of maize growth and development, 1st ed. ed. Texas A&M University Press, College Station.
Jones, J.W., Hoogenboom, G., Porter, C.H., Boote, K.J., Batchelor, W.D., Hunt, L.A., Wilkens, P.W., Singh, U., Gijsman, A.J., Ritchie, J.T. 2003. The DSSAT cropping system model. Eur. J. Agron. 18, 235–265. https://doi.org/10.1016/S1161-0301(02)00107-7
Kogan, F.N. 1995. Application of vegetation index and brightness temperature for drought detection. Adv. Space Res. 15, 91–100. https://doi.org/10.1016/0273- 1177(95)00079-T
Liao, C., Wang, J., Dong, T., Shang, J., Liu, J., Song, Y. 2019. Using spatio-temporal fusion of Landsat-8 and MODIS data to derive phenology, biomass and yield estimates for corn and soybean. Sci. Total Environ. 650, 1707– 1721. https://doi.org/10.1016/j.scitotenv.2018.09.308
Lobell, D.B. 2013. The use of satellite data for crop yield gap analysis. Field Crops Res. 143, 56–64. https://doi.org/10.1016/j.fcr.2012.08.008
Monteith, J.L. 1972. Solar Radiation and Productivity in Tropical Ecosystems. J. Appl. Ecol. 9, 747. https://doi.org/10.2307/2401901
Rattalino Edreira, J.I., Mourtzinis, S., Azzari, G., Andrade, J.F., Conley, S.P., Lobell, D., Specht, J.E., Grassini, P. 2020a. From sunlight to seed: Assessing limits to solar radiation capture and conversion in agro-ecosystems. Agric. For. Meteorol. 280, 107775. https://doi.org/10.1016/j.agrformet.2019.107775
Rattalino Edreira, J.I., Mourtzinis, S., Azzari, G., Andrade, J.F., Conley, S.P., Specht, J.E., Grassini, P. 2020b. Combining field-level data and remote sensing to understand impact of management practices on producer yields. Field Crops Res. 257, 107932. https://doi.org/10.1016/j.fcr.2020.107932
Ruimy, A., Saugier, B., Dedieu, G. 1994. Methodology for the estimation of terrestrial net primary production from remotely sensed data. J. Geophys. Res. Atmospheres 99, 5263–5283. https://doi.org/10.1029/93JD03221
Schwalbert, R.A., Amado, T., Corassa, G., Pott, L.P., Prasad, P.V.V., Ciampitti, I.A. 2020. Satellite-based soybean yield forecast: Integrating machine learning and weather data for improving crop yield prediction in southern Brazil. Agric. For. Meteorol. 284, 107886. https://doi.org/10.1016/j.agrformet.2019.107886
Senay, G.B., Verdin, J. 2003. Characterization of yield reduction in Ethiopia using a GIS-based crop water balance model. Can. J. Remote Sens. 29, 687–692. https://doi.org/10.5589/m03-039
Severini, A.D., Álvarez-Prado, S., Otegui, M.E., Kavanová, M., Vega, C.R.C., Zuil, S., Ceretta, S., Acreche, M., Amarilla, F., Cicchino, M., Fernández-Long, M.E., Crespo, A., Serrago, R., Miralles, D.J. 2023. CRONOSOJA: a daily time-step hierarchical model predicting soybean development across maturity groups in the Southern Cone (preprint). Plant Biology. https://doi.org/10.1101/2023.09.18.558336
Skakun, S., Kalecinski, N.I., Brown, M.G.L., Johnson, D.M., Vermote, E.F., Roger, J.-C., Franch, B. 2021. Assessing within-Field Corn and Soybean Yield Variability from WorldView-3, Planet, Sentinel-2, and Landsat 8 Satellite Imagery. Remote Sens. 13, 872. https://doi.org/10.3390/rs13050872
Sparks, A. 2018. nasapower: A NASA POWER Global Meteorology, Surface Solar Energy and Climatology Data Client for R. J. Open Source Softw. 3, 1035. https://doi.org/10.21105/joss.01035
Spennemann, P.C., Fernández-Long, M.E., Gattinoni, N.N., Cammalleri, C., Naumann, G. 2020. Soil moisture evaluation over the Argentine Pampas using models satellite estimations and in-situ measurements. J. Hydrol. Reg. Stud. 31, 100723. https://doi.org/10.1016/j.ejrh.2020.100723
Stepanov, A., Dubrovin, K., Sorokin, A., Aseeva, T. 2020. Predicting Soybean Yield at the Regional Scale Using Remote Sensing and Climatic Data. Remote Sens. 12, 1936. https://doi.org/10.3390/rs12121936
Tucker, C.J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8, 127–150. https://doi.org/10.1016/0034-4257(79)90013-0
Van Der Velde, M., Nisini, L. 2019. Performance of the MARS-crop yield forecasting system for the European Union: Assessing accuracy, in-season, and year-toyear improvements from 1993 to 2015. Agric. Syst. 168, 203–212. https://doi.org/10.1016/j.agsy.2018.06.009
Van Der Velde, M., Van Diepen, C.A., Baruth, B. 2019. The European crop monitoring and yield forecasting system: Celebrating 25 years of JRC MARS Bulletins. Agric. Syst. 168, 56–57. https://doi.org/10.1016/j.agsy.2018.10.003
Van Ittersum, M.K., Cassman, K.G., Grassini, P., Wolf, J., Tittonell, P., Hochman, Z. 2013. Yield gap analysis with local to global relevance—A review. Field Crops Res. 143, 4–17. https://doi.org/10.1016/j.fcr.2012.09.009
Von Bloh, M., Nóia Júnior, R.D.S., Wangerpohl, X., Saltık, A.O., Haller, V., Kaiser, L., Asseng, S. 2023. Machine learning for soybean yield forecasting in Brazil. Agric. For. Meteorol. 341, 109670. https://doi.org/10.1016/j.agrformet.2023.109670
Zhuo, W., Huang, J., Li, L., Huang, R., Gao, X., Zhang, X., Zhu, D. 2018. Assimilating SAR and Optical Remote Sensing Data into WOFOST Model for Improving Winter Wheat Yield Estimation, in: 2018 7th International Conference on Agro-Geoinformatics (Agro-Geoinformatics). Presented at the 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics), IEEE, Hangzhou, pp. 1–5. https://doi.org/10.1109/AgroGeoinformatics.2018.8476074
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