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The Permafrost Grown project (NSF RISE Award # 2126965) is co-producing knowledge with farmers in Alaska (Tanana Valley and Bethel) to investigate the interactions and feedbacks between permafrost and agriculture. Additional project objectives include understanding legacy effects over a 120-year cultivation history in the Tanana Valley, evaluating the socio-economic effects of permafrost-agriculture interactions and provide decision making tools for farmers and finally to utilize education and outreach activities to share knowledge with the farmers and the public. The project focuses on in-the-ground farming in a range of cultivation types including crops, peonies and livestock. The project is funded through the National Science Foundation's (NSF) Navigating the New Arctic Initiative. Temperature monitoring of various crop types with and without extension techniques was done at two farm sites in Fairbanks, Alaska (AK) during the 2022 growing season. This work was done through the Permafrost Grown Project as part of an effort to determine the thermal impact of commonly used agricultural seasonal-extension techniques, crop types and their potential impact on permafrost. Both farms are small scale, each cultivating on about 1 acre and both grow diverse crops. Both farms use various season extension techniques, including the use of plastic mulch to artificially warm soils and/or help control weeds. This dataset provides monitoring of ground temperatures at four depths (ground surface, 15 centimeter (cm), 50 cm and 100 cm) of various crops (carrots, cabbage, beets, onions, and squash).
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Fine-Scale (10 m) Dynamics of Smallholder Farming through COVID-19 in Eastern Thailand
This study aims to understand the spatiotemporal changes in patterns of tropical crop cultivation in Eastern Thailand, encompassing the periods before, during, and after the COVID-19 pandemic. Our approach involved assessing the efficacy of high-resolution (10 m) Sentinel-2 dense image time series for mapping smallholder farmlands. We integrated harmonic regression and random forest to map a diverse array of tropical crop types between summer 2017 and summer 2023, including durian, rice, rubber, eucalyptus, oil palm, pineapple, sugarcane, cassava, mangosteen, coconut, and other crops. The results revealed an overall mapping accuracy of 85.6%, with several crop types exceeding 90%. High-resolution imagery demonstrated particular effectiveness in situations involving intercropping, a popular practice of simultaneously growing two or more plant species in the same patch of land. However, we observed overestimation in the majority of the studied cash crops, primarily those located in young plantations with open tree canopies and grass-covered ground surfaces. The adverse effects of the COVID-19 pandemic were observed in specific labor-intensive crops, including rubber and durian, but were limited to the short term. No discernible impact was noted across the entirety of the study timeframe. In comparison, financial gain and climate change appeared to be more pivotal in influencing farmers’ decisions regarding crop cultivation. Traditionally dominant crops such as rice and oil palm have witnessed a discernible decline in cultivation, reflecting a decade-long trend of price drops preceding the pandemic. Conversely, Thai durian has seen a significant upswing even over the pandemic, which ironically served as a catalyst prompting Thai farmers to adopt e-commerce to meet the surging demand, particularly from China.
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- Award ID(s):
- 2153579
- PAR ID:
- 10503460
- Publisher / Repository:
- MPDI
- Date Published:
- Journal Name:
- Remote Sensing
- Volume:
- 16
- Issue:
- 6
- ISSN:
- 2072-4292
- Page Range / eLocation ID:
- 1035
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/rs16061035
