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Agronomy
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Agronomic Practices and Strategies for Cropping System Optimization in a...
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Agronomic Practices and Strategies for Cropping System Optimization in a Climate Change Scenario

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Farming Sustainability".

Deadline for manuscript submissions: closed (15 April 2023) | Viewed by 7860

Special Issue Editor

Dr. Stefano Marino

E-Mail Website
Guest Editor
Department of Agricultural, Environmental and Food Sciences, University of Molise, Campobasso, Italy
Interests: response of agronomic crops to various environmental and nutritional stresses; effects of nitrogen, water, and weed stress on crop biomass, yield, and yield components; cereal crops (wheat, barley, and spelt); vegetable crops (tomatoes, onions, beans, melons, and watermelons); protein crops (beans, peas, chickpeas, and grass peas); tubers (potatoes); innovative field-scale techniques for the early detection of stress utilizing spectral reflectance and vegetation indices
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Agronomic practices could help to achieve the goals of improvement of soil fertility, the rational design of cropping systems, water-saving technology, the development of efficient fertilizers, and weed management. Climate change increased the frequency and intensity of extreme weather—climate events can adversely affect crop production and management strategies in many parts of the world. Many agronomic, technological, and ecological advancements are underway in full force, and these efforts need to be integrated for a sustainable crop management program to materialize. Furthermore, smart agriculture, geospatial, information technology, the Internet of Things, robotics, artificial intelligence, data analytics applications, and digital web applications play essential roles in modern farm management. Traditional and innovative agronomic approaches of information and knowledge for the monitoring and management of agricultural fields can address the new agricultural challenges related to cropping system optimization for improving productivity and sustainability, thus reducing environmental impacts.

This Special Issue calls for contributions on cropping system optimization. Studies and best practices on irrigation use efficiency, weed management, nutrient management, and agronomic strategies for improving crop productivity and environmental sustainability are welcome.

Dr. Stefano Marino
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agronomy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cropping system
  • crop bioclimatic models
  • weed detection and management
  • irrigation water demand management (scheduling tools, irrigation system)
  • soil fertility
  • nutrient management
  • remote sensing (satellite, airborne, UAV Imagery, and proximal sensing)
  • smart agriculture
  • life cycle assessment (LCA)

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Published Papers (2 papers)

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Research

13 pages, 2679 KiB  
Article
Life Cycle Assessment for Soybean Supply Chain: A Case Study of State of Pará, Brazil
by Thyago Brito, Rui Fragoso, Leovigildo Santos, José António Martins, Anabela Afonso Fernandes Silva and José Aranha
Agronomy 2023, 13(6), 1648; https://doi.org/10.3390/agronomy13061648 - 19 Jun 2023
Cited by 5 | Viewed by 4599
Abstract
Brazil has emerged as the world’s largest soybean producer and exporter in recent years. In the Brazilian Amazon Biome, the state of Pará has become a new agricultural frontier over the last two decades due to a significant increase in soybean cultivation throughout [...] Read more.
Brazil has emerged as the world’s largest soybean producer and exporter in recent years. In the Brazilian Amazon Biome, the state of Pará has become a new agricultural frontier over the last two decades due to a significant increase in soybean cultivation throughout its territory. However, it is essential to understand the associated effects on the environment at every point in the supply chain. This research aims to measure the effects on the environment of the soybean supply chain of two production poles utilising openLCA software and the life cycle assessment (LCA) methodology in the northeast (Paragominas) and south (Redenção) of the state of Pará in Brazil. In addition, we determine which is the most efficient route between the shipment port and the ultimate destination. The Recipe Midpoint (H) and Intergovernmental Panel on Climate Change (IPCC) methods of environmental impact categories were used in accordance with the cradle-to-grave scope. The BRLUC regionalised model (v1.3) was used to quantify land use change (LUC). According to the observed results, LUC was primarily responsible (between 3.8 and 32.69 tCO2 Eq·ha−1·year−1) for the global warming potential (GWP) of the soybean supply chain when rainforest-occupied land was converted into cropland. The soybean harvest in the Redenção pole is better loaded through the port of Itaqui (TEGRAM), which is in São Luis (state of Maranhão), due to the use of multiple modes of transport (lorry + train), allowing for better logistical performance and less impact on the environment, despite the longest distance (road + railway = 1306 km). Due to the short road distance (approximately 350 km) and consequently lower environmental impact, soybean harvested in the Paragominas pole is better loaded through the ports around Barcarena in the state of Pará. Full article
(This article belongs to the Special Issue Agronomic Practices and Strategies for Cropping System Optimization in a Climate Change Scenario)
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19 pages, 5955 KiB  
Article
Assessing the Agronomic Subfield Variability by Sentinel-2 NDVI Time-Series and Landscape Position
by Stefano Marino
Agronomy 2023, 13(1), 44; https://doi.org/10.3390/agronomy13010044 - 22 Dec 2022
Cited by 5 | Viewed by 2544
Abstract
Optimizing crop yield is one of the main focuses of precision farming. Variability in crop within a field can be influenced by many factors and it is necessary to better understand their interrelationships before precision management methods can be successfully used to optimize [...] Read more.
Optimizing crop yield is one of the main focuses of precision farming. Variability in crop within a field can be influenced by many factors and it is necessary to better understand their interrelationships before precision management methods can be successfully used to optimize yield and quality. In this study, NDVI time-series from Sentinel-2 imagery and the effects of landscape position, topographic features, and weather conditions on agronomic spatial variability of crop yields and yield quality were analyzed. Landscape position allowed the identification of three areas with different topographic characteristics. Subfield A performed the best in terms of grain yield, with a mean yield value 10% higher than subfield B and 35% higher than subfield C, and the protein content was significantly higher in area A. The NDVI derived from the Sentinel-2 data confirms the higher values of area A, compared to subfields B and C, and provides useful information about the lower NDVI cluster in the marginal areas of the field that are more exposed to water flow in the spring season and drought stress in the summer season. Landscape position analysis and Sentinel-2 data can be used to identify high, medium, and low NDVI values differentiated for each subfield area and associated with specific agronomic traits. In a climate change scenario, NDVI time-series and landscape position can improve the agronomic management of the fields. Full article
(This article belongs to the Special Issue Agronomic Practices and Strategies for Cropping System Optimization in a Climate Change Scenario)
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