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Land
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Land Transformation, Climate Change and Agroecosystems Response and Adaptation
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Land Transformation, Climate Change and Agroecosystems Response and Adaptation

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land Systems and Global Change".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 4666

Special Issue Editors

Prof. Dr. Zeyuan Qiu

E-Mail Website
Guest Editor
Department of Chemistry and Environmental Science, College of Science and Liberal Arts, New Jersey Institute of Technology, Newark, NJ 07102, USA
Interests: land use change; climate cahnge; ecosystem services; watershed management; nonpint source pollution; stormwater management; green infrastructure
Special Issues, Collections and Topics in MDPI journals
Dr. Subhasis Giri

E-Mail Website
Guest Editor
Department of Ecology, Evolution, and Natural Resources, Rutgers, The State University of New Jersey, 14 College Farm Rd, New Brunswick, NJ 08901, USA
Interests: watershed hydrology; water quality; green infrastructure; ecohydrology; climate change; water sustainability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Agroecosystems are manmade ecosystems created for farming, animal husbandry, and fishing to meet the essential needs of humanity for food, fuel, and fiber. As the largest manmade ecosystems, they are constantly interacting with other natural and manmade ecosystems to affect the provision of the ecosystem services that are critical to planetary health and human well-beings. The state of agroecosystems and their capacity to provide variety of ecosystem services directly and indirectly affects the attainment of all 17 Sustainable Development Goals (SDGs) defined by the United Nations, most notably no poverty (SDG 1), zero hunger (SDG 2), good health and well-being (SDG 3), clean water and sanitation (SDG 6), affordable and clean energy (SDG 7), climate action (SDG 13), life below water (SDG 14) and life on land (SDG 15).   

The world population has reached 8.7 billion and will continues to grow. Future development of agroecosystems faces tremendous challenges. First, the growth of agroecosystems has already breached multiple planetary boundaries. Second, the structure and functions of agroecosystems are constantly shaped  by large scale land transformation such as urbanization, industrial development, deforestation, desertification and  land degradation. Third, agroecosystems are highly vulnerable to climate change and the elevated climate risk further degrades agroecosystems and their ecosystem services. The impacts of land transformation and climate change on agroecosystems and their ecosystem services are further complicated by technological innovation, economic development, geopolitics and regulations and policies. Meanwhile, agroecosystems are changing to adapt to the evolving social, economic, environmental, and technological environments to demonstrate their resilience. The notable examples include fast growing urban agriculture, agriculture in controlled environment and regenerative farming.

For the Special Issue on “Land Transformation, Climate Change, and Agroecosystem Response and Adaptation” we invite researchers, academics, practitioners, and experts to submit a wide range of interdisciplinary contributions, case studies, and methodological and applied research to explore the intricate relationship between land use changes, climate change impacts, and the adaptive responses of agricultural ecosystems at various scales ranging from a field to global scale. Through this interdisciplinary inquiry, we aim to shed light on the challenges and opportunities in sustainable land management and agricultural practices to mitigate climate risks while ensuring food security and achieving SDGs. Submissions may explore but are not limited to the following topics:

  1. Assessing the influence of land use transformations, such as deforestation, urbanization, and agricultural expansion, on agroecosystems and ecosystem services;
  2. Analyzing the effects of changing climatic conditions on crop productivity, livestock health, and overall agricultural resilience;
  3. Investigating innovative and practical strategies for farmers, policymakers, and communities to adapt to climate change impacts and mitigate associated risks;
  4. Exploring the role of ecosystem-based approaches, such as agroforestry, conservation agriculture, and sustainable water management, in enhancing agricultural resilience and reducing vulnerability to climate-related hazards;
  5. Evaluating the effectiveness of national and international policies and governance mechanisms in promoting sustainable and equitable land use practices and climate-smart agriculture;
  6. Showcasing advancements in agricultural technologies and their potential to enhance climate resilience and sustainable land management.

This call for paper proposals offers a unique opportunity to contribute to the growing body of knowledge on the critical relationship between land transformation, climate risk, and agroecosystem response and adaptation. We anticipate that the research presented in this special issue will inspire informed decisions and innovative solutions to address the challenges posed by climate change on agroecosystems while fostering sustainability and resilience.

We look forward to your valuable contributions to this important discourse.

Prof. Dr. Zeyuan Qiu
Dr. Subhasis Giri
Guest Editors

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. Land 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

  • agroecosystems
  • ecosystem services
  • urban agriculture
  • land use change
  • resilience and adaptation
  • climate change
  • climate-smart agriculture

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Research

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19 pages, 6418 KiB  
Article
Evaluating Sugarcane Yield Estimation in Thailand Using Multi-Temporal Sentinel-2 and Landsat Data Together with Machine-Learning Algorithms
by Jaturong Som-ard, Savittri Ratanopad Suwanlee, Dusadee Pinasu, Surasak Keawsomsee, Kemin Kasa, Nattawut Seesanhao, Sarawut Ninsawat, Enrico Borgogno-Mondino and Filippo Sarvia
Land 2024, 13(9), 1481; https://doi.org/10.3390/land13091481 - 13 Sep 2024
Viewed by 941
Abstract
Updated and accurate crop yield maps play a key role in the agricultural environment. Their application enables the support for sustainable agricultural practices and the formulation of effective strategies to mitigate the impacts of climate change. Farmers can apply the maps to gain [...] Read more.
Updated and accurate crop yield maps play a key role in the agricultural environment. Their application enables the support for sustainable agricultural practices and the formulation of effective strategies to mitigate the impacts of climate change. Farmers can apply the maps to gain an overview of the yield variability, improving farm management practices and optimizing inputs to increase productivity and sustainability such as fertilizers. Earth observation (EO) data make it possible to map crop yield estimations over large areas, although this will remain challenging for specific crops such as sugarcane. Yield data collection is an expensive and time-consuming practice that often limits the number of samples collected. In this study, the sugarcane yield estimation based on a small number of training datasets within smallholder crop systems in the Tha Khan Tho District, Thailand for the year 2022 was assessed. Specifically, multi-temporal satellite datasets from multiple sensors, including Sentinel-2 and Landsat 8/9, were involved. Moreover, in order to generate the sugarcane yield estimation maps, only 75 sampling plots were selected and surveyed to provide training and validation data for several powerful machine-learning algorithms, including multiple linear regression (MLR), stepwise multiple regression (SMR), partial least squares regression (PLS), random forest regression (RFR), and support vector regression (SVR). Among these algorithms, the RFR model demonstrated outstanding performance, yielding an excellent result compared to existing techniques, achieving an R-squared (R2) value of 0.79 and a root mean square error (RMSE) of 3.93 t/ha (per 10 m × 10 m pixel). Furthermore, the mapped yields across the region closely aligned with the official statistical data from the Office of the Cane and Sugar Board (with a range value of 36,000 ton). Finally, the sugarcane yield estimation model was applied to over 2100 sugarcane fields in order to provide an overview of the current state of the yield and total production in the area. In this work, the different yield rates at the field level were highlighted, providing a powerful workflow for mapping sugarcane yields across large regions, supporting sugarcane crop management and facilitating decision-making processes. Full article
(This article belongs to the Special Issue Land Transformation, Climate Change and Agroecosystems Response and Adaptation)
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14 pages, 2504 KiB  
Article
Association between Outlying Values in Body Condition Indices in Small Mammals and Their Habitats
by Linas Balčiauskas and Laima Balčiauskienė
Land 2024, 13(8), 1271; https://doi.org/10.3390/land13081271 - 12 Aug 2024
Viewed by 604
Abstract
Habitat type and habitat change are very important factors in the body condition of small mammals that inhabit them. The response can be positive, increasing, or the opposite, decreasing body condition. We analyzed outliers of the body condition indices (BCIs) of 12 species [...] Read more.
Habitat type and habitat change are very important factors in the body condition of small mammals that inhabit them. The response can be positive, increasing, or the opposite, decreasing body condition. We analyzed outliers of the body condition indices (BCIs) of 12 species trapped in nine different habitats during 1980–2023 in Lithuania, a mid-latitude country. Mixed and fragmented habitats, as well as commensal habitats, could be considered the least suitable for small mammals, based on the highest proportions of underfit and low proportions of best-fit individuals. On the contrary, meadows and disturbed habitats (landfills and cormorant colonies) had the highest proportions of best-fit individuals, while the proportion of under-fit individuals was much lower than expected. We found outliers in the BCI in all species, except for the under-fit harvest mice (Micromys minutus), and in all habitats, though not numerous. The presence of the highest BCI in yellow-necked mice (Apodemus flavicollis) and bank voles (Clethrionomys glareolus) in the disturbed habitats studied and in house mice (Mus musculus) in commensal habitats may be related to the resources provided by these habitats. Our results demonstrate the feasibility of using retrospective small mammal morphometric data to analyze their relationship with habitat. Full article
(This article belongs to the Special Issue Land Transformation, Climate Change and Agroecosystems Response and Adaptation)
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18 pages, 10113 KiB  
Article
MaxEnt-Based Potential Distribution Mapping and Range Shift under Future Climatic Scenarios for an Alpine Bamboo Thamnocalamus spathiflorus in Northwestern Himalayas
by Rajendra K. Meena, Maneesh S. Bhandari, Pawan Kumar Thakur, Nitika Negi, Shailesh Pandey, Rama Kant, Rajesh Sharma, Netrananda Sahu and Ram Avtar
Land 2024, 13(7), 931; https://doi.org/10.3390/land13070931 - 26 Jun 2024
Cited by 1 | Viewed by 1035
Abstract
Thamnocalamus spathiflorus is a shrubby woody bamboo invigorating at the alpine and sub-alpine region of the northwestern Himalayas. The present investigation was conducted to map the potential distribution of Th. spathiflorus in the western Himalayas for current and future climate scenario using Ecological [...] Read more.
Thamnocalamus spathiflorus is a shrubby woody bamboo invigorating at the alpine and sub-alpine region of the northwestern Himalayas. The present investigation was conducted to map the potential distribution of Th. spathiflorus in the western Himalayas for current and future climate scenario using Ecological Niche Modelling (ENM). In total, 125 geo-coordinates were collected for the species presence from Himachal Pradesh (HP) and Uttarakhand (UK) states of India and modelled to predict the current distribution using the Maximum Entropy (MaxEnt) model, along with 13 bioclimatic variables selected after multi-collinearity test. Model output was supported with a significant value of the Area Under the “Receiver Operating Characteristics” Curve (AUC = 0.975 ± 0.019), and other confusion matrix-derived accuracy measures. The variables, namely precipitation seasonality (Bio 15), precipitation (Prec), annual temperature range (Bio 7), and altitude (Alt) showed highest level of percentage contribution (72.2%) and permutation importance (60.9%) in predicting the habitat suitability of Th. spathiflorus. The actual (1 km2 buffer zone) and predicted estimates of species cover were ~136 km2 and ~982 km2, respectively. The predicted range was extended from Chamba (HP) in the north to Pithoragarh (UK) in southeast, which further protracted to Nepal. Furthermore, the distribution modelling under future climate change scenarios (RCP 8.5) for year 2050 and 2070 showed an eastern centroidal shift with slight decline of the species area by ~16 km2 and ~46 km2, respectively. This investigation employed the Model for Interdisciplinary Research on Climate (MIROC6)–shared socio-economics pathways (SSP245) for cross-validation purposes. The model was used to determine the habitat suitability and potential distribution of Th. spathiflorus in relation to the current distribution and RCP 8.5 future scenarios for the years 2021–2040 and 2061–2080, respectively. It showed a significant decline in the distribution area of the species between year 2030 and 2070. Overall, this is the pioneer study revealing the eco-distribution prediction modelling of this important high-altitude bamboo species. Full article
(This article belongs to the Special Issue Land Transformation, Climate Change and Agroecosystems Response and Adaptation)
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25 pages, 1832 KiB  
Article
The Role of Climate Change Perceptions in Sustainable Agricultural Development: Evidence from Conservation Tillage Technology Adoption in Northern China
by Leshan Yu, Hengtong Shi, Haixia Wu, Xiangmiao Hu, Yan Ge, Leshui Yu and Wenyu Cao
Land 2024, 13(5), 705; https://doi.org/10.3390/land13050705 - 17 May 2024
Cited by 1 | Viewed by 850
Abstract
Encouraging the use of conservation tillage technology is a highly effective approach to safeguarding soil health, improving the environment, and promoting sustainable agricultural development. With the mounting concerns surrounding climate change, developing conservation tillage methods that facilitate sustainable agricultural growth has become an [...] Read more.
Encouraging the use of conservation tillage technology is a highly effective approach to safeguarding soil health, improving the environment, and promoting sustainable agricultural development. With the mounting concerns surrounding climate change, developing conservation tillage methods that facilitate sustainable agricultural growth has become an imperative both in China and around the world. While it is widely recognized that adapting to climate change is crucial in agriculture, there is limited research on evaluating the risks, discovering resilience, measuring farmers’ perceptions on climate change, and exploring how tillage technology can be adjusted in the context of small-scale farming in China to foster sustainable development. Using research data from smallholder farmers in the Shaanxi and Shanxi provinces of China, this paper aims to explore the impact of climate change perceptions on farmers’ adoption of conservation tillage technologies based on an ordered Probit model. We found that farmers tend to refrain from embracing conservation tillage technology due to the presence of unclear and conflicting perceptions regarding climate change. Focus on short-term profitability and inadequate preparation hinder them from prioritizing adaptation. We recognized several measures that could help farmers adapt and thrive within the agricultural sector. Furthermore, we have validated the need for self-system moderation in promoting farmers’ adoption of conservation tillage technology. By utilizing such tools and resources, farmers can comprehend the gravity of climate change’s impact on agricultural productivity and, more importantly, channel their efforts towards fortifying resilience to extreme weather conditions and long-term climate risks, thus fortifying agricultural sustainability. Full article
(This article belongs to the Special Issue Land Transformation, Climate Change and Agroecosystems Response and Adaptation)
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Review

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21 pages, 7250 KiB  
Review
Use of an Adaptive-Vegetation Model to Restore Degraded Tropical Peat Swamp Forest to Support Climate Resilience
by I. Wayan Susi Dharmawan, Yunita Lisnawati, Hengki Siahaan, Bambang Tejo Premono, Mohamad Iqbal, Ahmad Junaedi, Niken Sakuntaladewi, Bastoni, Ridwan Fauzi, Ramawati, Ardiyanto Wahyu Nugroho, Ni Kadek Erosi Undaharta, Anang Setiawan Achmadi, Titiek Setyawati, Chairil Anwar Siregar, Pratiwi, Sona Suhartana, Soenarno, Dulsalam and Asep Sukmana
Land 2024, 13(9), 1377; https://doi.org/10.3390/land13091377 - 28 Aug 2024
Viewed by 456
Abstract
Climate change poses significant challenges to ecosystems globally, demanding innovative methods for environmental conservation and restoration. Restoration initiatives require significant amounts of appropriate vegetation that is both adaptive and tolerant to the specific environmental factors. This study introduces an adaptive-vegetation model designed to [...] Read more.
Climate change poses significant challenges to ecosystems globally, demanding innovative methods for environmental conservation and restoration. Restoration initiatives require significant amounts of appropriate vegetation that is both adaptive and tolerant to the specific environmental factors. This study introduces an adaptive-vegetation model designed to support ecosystem resilience in the face of climate change. Traditional restoration methods often neglect dynamic environmental conditions and ecosystem interactions, but the model employs real-time data and predictive analytics to adapt strategies to evolving climate variables. The model takes a comprehensive approach, incorporating climate projections, soil health metrics, species adaptability, and hydrological patterns to inform restoration practices. By using a mix of adaptable native species, the model promotes biodiversity. In conclusion, according to the findings of our review, paludiculture and agroforestry could be implemented as models for improving climate resilience, particularly in tropical degraded peat swamp forests. These two models could improve the environment, the economy, and social functions. Finally, improving all three of these factors improves ecological stability. This adaptive-vegetation model represents a significant shift from static, uniform restoration approaches to dynamic, data-driven strategies tailored to specific environments. The future research directions underscore the need for ongoing innovation in conservation practices to safeguard ecosystems amid unprecedented environmental changes. Future efforts will focus on enhancing the model with advanced machine learning techniques and expanding its application to additional ecological contexts. Full article
(This article belongs to the Special Issue Land Transformation, Climate Change and Agroecosystems Response and Adaptation)
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