DYNAMIC RECOMMENDATION OF N IN MAIZE BASED ON WATER PREDICTION TRANSPIRED

Authors

  • J. Vargas Instituto Nacional de Investigación Agropecuaria (INIA), estación experimental La Estanzuela, Ruta 50 km 11, El Semillero, Colonia, Uruguay
  • M. C. Capurro Instituto Nacional de Investigación Agropecuaria (INIA), estación experimental La Estanzuela, Ruta 50 km 11, El Semillero, Colonia, Uruguay
  • A. Otero Instituto Nacional de Investigación Agropecuaria (INIA), estación experimental Salto Grande, Camino al Terrible, Salto, Uruguay
  • N. Maltese Instituto Nacional de Investigación Agropecuaria (INIA), estación experimental La Estanzuela, Ruta 50 km 11, El Semillero, Colonia, Uruguay
  • A. G. Berger Instituto Nacional de Investigación Agropecuaria (INIA), estación experimental La Estanzuela, Ruta 50 km 11, El Semillero, Colonia, Uruguay

Keywords:

predicción, agua transpirada, maíz, nitrógeno

Abstract

The objective of the present work is to study the stability of the relationship between N absorbed and water transpired in order to explore new tools for predicting nitrogen fertilization needs based on the prediction of N demand by this route. If this tool is reliable and can be extrapolated to different environments, it would be very useful for rational and efficient use of N in scenarios of different water availability.

References

Allen, R.G., Pereira, L.S., Smith, M., Raes, D., Wright, J.L. 2005. FAO-56 Dual Crop Coefficient Method for Estimating Evaporation from Soil and Application Extensions. J. Irrig. Drain Eng. 131, 2–13. https://doi.org/10.1061/(ASCE)0733-9437(2005)131:1(2)

Zeileis, A., Grothendieck, G. 2005. “zoo: S3 Infrastructure for Regular and Irregular Time Series.” Journal of Statistical Software, 14(6), 1–27. doi:10.18637/jss.v014.i06.

Forkel, M., Wutzler, T. 2015. Greenbrown - land surface phenology and trend analysis. A package for the R software. Version 2.2, 2015-04-15, http://greenbrown.r-forge.r-project.org/.

Beck, P.S.A., Atzberger, C., Høgda, K.A., Johansen, B., Skidmore, A.K. 2006. Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI. Remote Sensing of Environment 100, 321–334. https://doi.org/10.1016/j.rse.2005.10.021

Cassman, K.G., Dobermann, A., Walter,s D.T. 2002. Agroecosystems, nitrogen-use efficiency, and nitrogen management. Ambio. Mar;31(2):132-40. doi: 10.1579/0044-7447-31.2.132. PMID: 12078002.

Ciampitti, I.A., Fernández, J., Tamagno, S., Zhao, B., Lemaire, G., Makowski, D. 2021. Does the critical N dilution curve for maize crop vary across genotype x environment x management scenarios? - a Bayesian analysis. European Journal of Agronomy 123, 126202. https://doi.org/10.1016/j.eja.2020.126202

Correndo, A.A., et al. 2022. Metrica: an R package to evaluate prediction performance of regression and classification point-forecast models. Journal of Open Source Software, 7(79), 4655, https://doi.org/10.21105/joss.04655. Resultados de la Encuesta Agrícola “Invierno 2022,”.

Fernández, J.A., DeBruin, J., Messina, C.D., Ciampitti, I.A., 2020. Late-season nitrogen fertilization on maize yield: A meta-analysis. Field Crops Research 247, 107586. https://doi.org/10.1016/j.fcr.2019.107586

Grahmann, K., Rubio Dellepiane, V., Terra, J.A., Quincke, J.A. 2020. Long-term observations in contrasting crop-pasture rotations over half a century: Statistical analysis of chemical soil properties and implications for soil sampling frequency. Agriculture, Ecosystems & Environment 287, 106710. https://doi.org/10.1016/j.agee.2019.106710

Johnson, L.F., Trout, T.J. 2012. Satellite NDVI Assisted Monitoring of Vegetable Crop Evapotranspiration in California’s San Joaquin Valley. Remote Sensing 4, 439–455. https://doi.org/10.3390/rs4020439

Hernández, M.D., Alfonso, C., Echarte, M.M., Cerrudo, A., Echarte, L. 2021. Maize transpiration efficiency increases with N supply or higher plant densities. Agricultural Water Management 250, 106816. https://doi.org/10.1016/j.agwat.2021.106816

Kunrath, T.R., Lemaire, G., Sadras, V.O., Gastal, F. 2018. Water use efficiency in perennial forage species: Interactions between nitrogen nutrition and water deficit. Field Crops Research 222, 1–11. https://doi.org/10.1016/j.fcr.2018.02.031

Lemaire, G. (Ed.) 1997. Diagnosis of the Nitrogen Status in Crops. Springer Berlin Heidelberg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-60684-7

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

Lemaire, G., Sinclair, T., Sadras, V., Bélanger, G. 2019. Allometric approach to crop nutrition and implications for crop diagnosis and phenotyping. A review. Agron. Sustain. Dev. 39, 27. https://doi.org/10.1007/s13593-019-0570-6

R Core Team. 2021. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

Richards, L. A. 1956. Sample retainers for measuring water retention by soil. Soil Sci. Soc. Am. Proc. 20: 301–303.

Richards, L. A., and Weaver, L. R. 1944. Moisture retention by some irrigated soils as related to soil moisture tension. J. Agr. Res. 69: 215–235.

Ritchie, S.W. and Hanway, J.J. 1982. How a Corn Plant Develops. Special Report No. 48, Iowa State University of Science and Technology, Cooperative Extension Service.

Rizzo, G., Mazzilli, S.R., Ernst, O., Baethgen, W.E., Berger, A.G. 2022. Season-specific management strategies for rainfed soybean in the South American Pampas based on a seasonal precipitation forecast. Agricultural Systems 196, 103331. https://doi.org/10.1016/j.agsy.2021.103331

Sinclair, T.R., Rufty, T.W. 2012. Nitrogen and water resources commonly limit crop yield increases, not necessarily plant genetics. Global Food Security 1, 94–98. https://doi.org/10.1016/j.gfs.2012.07.001

Steduto, P., Hsiao, T.C., Fereres, E. 2007. On the conservative behavior of biomass water productivity. Irrig Sci 25, 189–207. https://doi.org/10.1007/s00271-007-0064-1

Thorp, K.R. 2022. pyfao56: FAO-56 evapotranspiration in Python. SoftwareX 19, 101208. https://doi.org/10.1016/j.softx.2022.101208

Trout, T.J., DeJonge, K.C. 2018. Crop Water Use and Crop Coefficients of Maize in the Great Plains. J. Irrig. Drain Eng. 144, 04018009. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001309

Weiner, J. 2004. Allocation, plasticity and allometry in plants. Perspectives in Plant Ecology, Evolution and Systematics 6, 207–215. https://doi.org/10.1078/1433-8319-00083

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Published

2024-05-28

Issue

No. Edición Especial (2024): "12th Public-Private Exchange Workshop (JIAPP)"

Section

Reports of results/advances

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Copyright (c) 2024 J. Vargas, M. C. Capurro, A. Otero, N. Maltese, A. G. Berger

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How to Cite

DYNAMIC RECOMMENDATION OF N IN MAIZE BASED ON WATER PREDICTION TRANSPIRED. (2024). Nexo Agropecuario, Edición Especial, 45-53. https://revistas.unc.edu.ar/index.php/nexoagro/article/view/45178