Smart Field Practices for Next-Generation Agricultural Production Systems

A special issue of Land (ISSN 2073-445X).

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 2213

Special Issue Editors


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Guest Editor
1. Amrita School of Agricultural Sciences, Amrita Vishwa Vidyapeetham, Coimbatore 641112, India
2. UWA School of Agriculture and Environment, The University of Western Australia, M082, Locked Bag 5005, Perth, WA 6001, Australia
Interests: greenhouse gas emission; herbicide resistance evolution; weed ecology; agronomy; artificial intelligence and image processing
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Guest Editor
Amrita School of Agricultural Sciences, Amrita Vishwa Vidyapeetham, J. P. Nagar, Arasampalayam, Myleripalayam, Coimbatore 642109, Tamil Nadu, India
Interests: remote sensing; GIS; crop growth simulation models; precision agriculture; irrigation management; crop yield prediction

Special Issue Information

Dear Colleagues,

New-age technologies have the potential to revolutionize agriculture by making it more sustainable, efficient, and productive. These technologies will allow farmers to optimize their use of resources, reduce waste, and improve transparency and traceability in the food supply chain. Precision agriculture, for example, enables farmers to target inputs like water, fertilizer, and pesticides to specific field areas, resulting in improved crop yields and reduced environmental impact. Field automation can also help farmers to reduce labour costs, increase productivity, and minimize environmental impact. By using artificial intelligence to analyze data from various sources, farmers can make more informed decisions about when to irrigate, fertilize, and harvest crops. IoT-based crop management utilizing blockchain technology in supply chain monitoring will ensure that the food produces is safe, high-quality, and transparently traced from farm to table. Next-generation agricultural practices have the potential to transform agriculture by enhancing sustainability, efficiency, and profitability while also improving the safety and quality of the food we consume.

This Special Issue calls for contributions that address smart field practices for next-generation agricultural systems that aim to optimize agricultural production by integrating the latest technologies while preserving and enhancing the natural resources of the environment. Alternatively, contributions that may examine the potential of digital agriculture to strengthen the efficiency of farm operations and improve decision-making and other related topics (see below) are highly welcome.

Topics include but are not limited to:

  1. Precision agriculture: This involves implementing advanced technologies such as drones and IoT sensors to gather data on soil, weather, and plant health, allowing farmers to make more informed decisions about planting, fertilizing, and harvesting crops.
  2. Smart irrigation protocols: New-age technologies to optimize irrigation systems and improve water use efficiency in agriculture. The use of sensors, controllers, and software to monitor soil moisture, weather conditions, and crop water needs in real-time.
  3. Integrated pest, disease, and nutrient management: Decision support tools and models that integrate biological, cultural, and chemical methods to manage pests and diseases while supplementing plants with adequate nutrients and practicing conservation agriculture.
  4. Energy smart farming: Using renewable energy sources to power agricultural operations while reducing energy consumption and greenhouse gas emissions. Recent integrated approaches (e.g., agrivoltaics) can potentially address agricultural and energy issues simultaneously.

This Special Issue aims to support the development of more sustainable and efficient agricultural systems that benefit both farmers and the environment.

All types of scientific contributions, including empirical studies, research articles, and critical reviews related to the above topics, are welcome for inclusion in this publication.

Prof. Dr. Sudheesh Manalil
Dr. V.S. Manivasagam
Guest Editors

Manuscript Submission Information

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Published Papers (1 paper)

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Research

19 pages, 4786 KiB  
Article
Demonstrating the Use of the Yield-Gap Concept on Crop Model Calibration in Data-Poor Regions: An Application to CERES-Wheat Crop Model in Greece
by Melpomeni Nikou and Theodoros Mavromatis
Land 2023, 12(7), 1372; https://doi.org/10.3390/land12071372 - 08 Jul 2023
Viewed by 1183
Abstract
Yield estimations at global or regional spatial scales have been compromised due to poor crop model calibration. A methodology for estimating the genetic parameters related to grain growth and yield for the CERES-Wheat crop model is proposed based on yield gap concept, the [...] Read more.
Yield estimations at global or regional spatial scales have been compromised due to poor crop model calibration. A methodology for estimating the genetic parameters related to grain growth and yield for the CERES-Wheat crop model is proposed based on yield gap concept, the GLUE coefficient estimator, and the global yield gap atlas (GYGA). Yield trials with three durum wheat cultivars in an experimental farm in northern Greece from 2004 to 2010 were used. The calibration strategy conducted with CERES-Wheat (embedded in DSSAT v.4.7.5) on potential mode taking into account the year-to-year variability of relative yield gap Yrg (YgC_adj) was: (i) more effective than using the average site value of Yrg (YgC_unadj) only (the relative RMSE ranged from 10 to 13% for the YgC_adj vs. 48 to 57% for YgC_unadj) and (ii) superior (slightly inferior) to the strategy conducted with DSSAT v.4.7.5 (DSSAT v.3.5—relative RMSE of 5 to 8% were found) on rainfed mode. Earlier anthesis, maturity, and decreased potential yield (from 2.2 to 3.9% for 2021–2050, and from 5.0 to 7.1% for 2071–2100), due to increased temperature and solar radiation, were found using an ensemble of 11 EURO-CORDEX regional climate model simulations. In conclusion, the proposed strategy provides a scientifically robust guideline for crop model calibration that minimizes input requirements due to operating the crop model on potential mode. Further testing of this methodology is required with different plants, crop models, and environments. Full article
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