Nitrogen Management and Water-Nitrogen Interactions in Agriculture

A special issue of Nitrogen (ISSN 2504-3129).

Deadline for manuscript submissions: closed (31 May 2024) | Viewed by 2819

Special Issue Editor


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Guest Editor
Department of Stress and Plant Pathology, Spanish National Research Council, Murcia, Spain
Interests: environment; soil fertility; plant nutrition; soil; agronomy; carbon dioxide; fruit quality; hydroponics; nitrate; weed ecophysiology; salinity stress; atmospheric CO2 concentration

Special Issue Information

Dear Colleagues,

Water and nitrogen are probably the main external resources that most contribute to crop development and yield and whose best use defines the profitability and sustainability of many irrigated agricultural systems. In irrigated agriculture, nitrogen fertilizer management practices, i.e., the forms used, the time and place of application, as well as the amounts applied, are greatly influenced by the irrigation management practices, i.e., type of irrigation, irrigation design, irrigation frequency and irrigation rates. These two farming practices, irrigation and nitrogen fertilization and their joint management, contribute substantially to the higher or lower efficiency in the use of both resources, water and nutrients, and therefore to the sustainability of the agricultural system. In this Special Issue of the journal Nitrogen, we want to highlight the relevance of the joint management of water and nitrogen, how both factors can affect each other, and how necessary it is to integrate or consider both factors to discuss the sustainability of the agricultural system. Therefore, all those works that integrate both practices (irrigation and fertilization) for greater efficiency in the use of water and/or nitrogen will be welcome.

Dr. José Salvador Rubio-Asensio
Guest Editor

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Keywords

  • nitrogen
  • water-nitrogen interactions
  • nitrogen fertilizer management
  • nitrogen use efficiency (NUE)
  • water use efficiency (WUE)

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Published Papers (2 papers)

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Research

15 pages, 2912 KiB  
Article
Spectral Index-Based Estimation of Total Nitrogen in Forage Maize: A Comparative Analysis of Machine Learning Algorithms
by Aldo Rafael Martínez-Sifuentes, Ramón Trucíos-Caciano, Nuria Aide López-Hernández, Enrique Miguel-Valle and Juan Estrada-Ávalos
Nitrogen 2024, 5(2), 468-482; https://doi.org/10.3390/nitrogen5020030 - 29 May 2024
Cited by 1 | Viewed by 931
Abstract
Nitrogen plays a fundamental role as a nutrient for the growth of leaves and the process of photosynthesis, as it directly influences the quality and yield of corn. The importance of knowing the foliar nitrogen content through Machine Learning algorithms will help determine [...] Read more.
Nitrogen plays a fundamental role as a nutrient for the growth of leaves and the process of photosynthesis, as it directly influences the quality and yield of corn. The importance of knowing the foliar nitrogen content through Machine Learning algorithms will help determine the efficient use of nitrogen fertilization in a context of sustainable agronomic management by avoiding Nitrogen loss and preventing it from becoming a pollutant for the soil and the atmosphere. The combination of machine learning algorithms with vegetation spectral indices is a new practice that helps estimate parameters of agricultural importance such as nitrogen. The objective of the present study was to compare random forest and neural network algorithms for estimating total plant nitrogen with spectral indices. Five spectral indices were obtained from remotely piloted aircraft systems and analyzed by mean, maximum and minimum from each sample plot to finally obtain 15 indices, and total nitrogen was estimated from the georeferenced points. The most important variables were selected with backward, forward and stepwise methods and total nitrogen estimates by laboratory were compared with random forest models and artificial neural networks. The most important indices were NDREmax and TCARImax. Using 15 spectral indices, total nitrogen with a variance of 79% and 81% with random forest and artificial neural network, respectively, was estimated. And only using NDREmax and TCARmax indices, 73% and 79% were explained by random forest and artificial neural network, respectively. It is concluded that it is possible to estimate nitrogen in forage maize with two indices and it is recommended to analyze by phenological stage and with a greater number of field data. Full article
(This article belongs to the Special Issue Nitrogen Management and Water-Nitrogen Interactions in Agriculture)
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20 pages, 1297 KiB  
Article
Crop Rotation and Nitrogen Fertilizer on Nitrate Leaching: Insights from a Low Rainfall Study
by Isabeli P. Bruno, Augusto G. Araújo, Gustavo H. Merten, Audilei S. Ladeira and Victor M. Pinto
Nitrogen 2024, 5(2), 329-348; https://doi.org/10.3390/nitrogen5020022 - 19 Apr 2024
Cited by 1 | Viewed by 1217
Abstract
The intensive use of agricultural fertilizers containing nitrogen (N) can increase the risk of nitrate (NO3) leaching. However, little information exists regarding its interaction with other factors that influence NO3 leaching, such as no-tillage, which is associated with [...] Read more.
The intensive use of agricultural fertilizers containing nitrogen (N) can increase the risk of nitrate (NO3) leaching. However, little information exists regarding its interaction with other factors that influence NO3 leaching, such as no-tillage, which is associated with different crop rotation schemes. The objective of this study was to quantify the leachate NO3 concentration and load below the root zone in two different crop rotations under no-tillage, with and without mineral N fertilizer. The experiment was conducted in a no-tillage area in Brazil between 2018 and 2020. The factors were two crop rotations (diversified and simplified) and two N fertilization managements (with and without N fertilizer). The soil solution was collected with suction lysimeters (1 m depth), the NO3 concentration (mg L−1) was spectrophotometrically determined, and the NO3 load (kg ha−1) was calculated from the volume of water drained and the NO3 concentration. The results were categorized into 24 evaluation periods. NO3 leaching was extremely low due to low rainfall throughout the experiment, with no significant differences between the factors and treatments. In the presence of N fertilization, leaching was substantially greater when rainfall increased, and vice versa. No significant difference was observed between the crop rotation schemes, except for one period in which the simplified soybean rotation exhibited high leaching. The evaluated treatments showed less NO3 leaching during the four periods when grass species were cultivated, indicating the importance of grasses in rotation systems. Full article
(This article belongs to the Special Issue Nitrogen Management and Water-Nitrogen Interactions in Agriculture)
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