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Agronomy
Special Issues
Soil and Water Management and Conservation in Regenerative Agriculture
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Soil and Water Management and Conservation in Regenerative Agriculture

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

Deadline for manuscript submissions: 28 February 2025 | Viewed by 1587

Special Issue Editor

Dr. Sanjit Deb

E-Mail Website
Guest Editor
Associate Professor, Department of Plant and Soil Science, Texas Tech University, Lubbock, TX 79409, USA
Interests: soil physics; hydrology; vadose zone flow and transport processes; soil and water management; soil–water–plant–atmosphere relationships; hydrological modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Regenerative agriculture (RA) has increasingly emerged as a promising, pragmatic, and holistic approach aimed at regenerating, restoring, or safeguarding essential agricultural resources (i.e., soil, water, biota, humans, and energy) to achieve sustainable agriculture. Despite the fact that a variety of researchers and practitioners perceive the definitions and descriptions of RA differently, a majority of RA practices are proposed as a solution towards sustainable agricultural production systems, with a growing emphasis on agricultural soil and water management, under the premise that these practices will restore and maintain soil health and fertility, protect water resources, improve climate resilience, support biodiversity, and enhance ecological and economic resilience. Scientific progress continues to advance our understanding of various soil and water management and conservation principles and practices to address challenges in RA, including soil health, water security and quality, food security, rising climate instability, and environmental sustainability.

Broadly, this Special Issue intends to cover the recent scientific developments in RA. This Special Issue aims primarily to identify knowledge gaps and research challenges in various important and challenging issues in soil and water management and conservation in RA to achieve more efficient use of soil and water resources in agriculture, mitigate the environmental and biodiversity impacts of agriculture, and strengthen the resilience and adaptation to climate change in agriculture. All contributions and article types (original research, reviews, technical notes, and communication) providing recent discoveries and new insights into various aspects of soil and water management and conservation in RA are encouraged. Theoretical, methodological, meta-analysis, modeling, and case study papers are welcome.

Dr. Sanjit Deb
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

  • sustainable agriculture
  • conservation agriculture
  • organic agriculture
  • soil health
  • soil water
  • soil amendments
  • irrigation and water management
  • agrohydrology
  • cover crops and crop rotation
  • conservation tillage
  • soil organic matter
  • carbon sequestration
  • agrobiodiversity
  • agroecology
  • agroforestry
  • climate-smart agriculture

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Published Papers (2 papers)

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15 pages, 2129 KiB  
Article
Assessing Yield, Biomass Production, and Forage Quality of Red Clover (Trifolium pratense L.) in Agroforestry System: One-Year Study in Szarvas, Hungary
by Zibuyile Dlamini, Mihály Jancsó, Árpád Székely, Ildikó Kolozsvári, Norbert Túri, Beatrix Bakti, Mihály Zalai and Ágnes Kun
Agronomy 2024, 14(9), 1921; https://doi.org/10.3390/agronomy14091921 - 27 Aug 2024
Viewed by 731
Abstract
This study examines the impact of line spacing (X: 24 m, Y: 9 m, Z: 6.5 m) and orientation to tree lines on the growth, yield, and quality of red clover (Trifolium pratense L.) in a temperate, irrigated agroforestry system (2 ha) [...] Read more.
This study examines the impact of line spacing (X: 24 m, Y: 9 m, Z: 6.5 m) and orientation to tree lines on the growth, yield, and quality of red clover (Trifolium pratense L.) in a temperate, irrigated agroforestry system (2 ha) in Szarvas, Hungary. Three sampling locations were distinguished between the east and west oriented tree lines: the north (N) side, middle (M) strip, and south (S) side of the tree lines. The highest red clovers were observed in the 6.5 m spacing (mean height 69.3 ± 7.2 cm), although yields were similar across 24 m, 9 m, and 6.5 m spacings (2.9 t ha−1, 2.3 t ha−1, and 2.7 t ha−1 dry matter, respectively). Orientation significantly influenced all forage quality parameters, with the north side showing earlier developmental stages and higher proportions of immature flowers (41–59%). Managing the spatial arrangement of red clover in agroforestry systems can help optimize forage quality by mitigating variations in plant maturity. Full article
(This article belongs to the Special Issue Soil and Water Management and Conservation in Regenerative Agriculture)
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12 pages, 8686 KiB  
Article
Detection of Straw Coverage under Conservation Tillage Based on an Improved Mask Regional Convolutional Neural Network (Mask R-CNN)
by Yuanyuan Shao, Xianlu Guan, Guantao Xuan, Hang Liu, Xiaoteng Li, Fengwei Gu and Zhichao Hu
Agronomy 2024, 14(7), 1409; https://doi.org/10.3390/agronomy14071409 - 28 Jun 2024
Viewed by 488
Abstract
Conservation tillage, a crucial method for protecting soil fertility, depends heavily on maintaining adequate straw coverage. The current method of straw coverage detection relies primarily on manual measurement, which is both time-consuming and laborious. This paper introduces a novel straw coverage detection approach [...] Read more.
Conservation tillage, a crucial method for protecting soil fertility, depends heavily on maintaining adequate straw coverage. The current method of straw coverage detection relies primarily on manual measurement, which is both time-consuming and laborious. This paper introduces a novel straw coverage detection approach based on an improved mask regional convolutional neural network (Mask R-CNN) algorithm. Several images of wheat straw-covered fields were taken, and the dataset was augmented using techniques like image inversion, contrast enhancement, Gaussian noise addition, and translation after cropping the original images. These fields use a crop rotation cycle of wheat and corn. Subsequently, the straw images were annotated using the Labelme annotation tool to obtain the available straw instance segmentation dataset. The Mask R-CNN algorithm was improved by refining the mask generation network structure through a multi-feature fusion strategy, which interweaves features from both the encoder and the mask generation network, enhancing the model’s ability to capture detailed and shape information of the straw. Lastly, using the mask information output by the improved Mask R-CNN algorithm, the straw coverage was calculated by counting the proportion of pixels within each segmented region. In the results, compared to the original Mask R-CNN algorithm, our improved Mask R-CNN algorithm achieved an average improvement of 7.8% in segmentation accuracy, indicating that the improved Mask R-CNN algorithm offers superior segmentation performance. Thus, the new algorithm can achieve straw coverage detection with higher accuracy and can provide a reference for other agricultural applications. Full article
(This article belongs to the Special Issue Soil and Water Management and Conservation in Regenerative Agriculture)
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