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. Data was collected at a small (less than one acre) farm that grows diverse crops. This farm has been impacted by subsidence from thawing ice-rich permafrost. The goal of the celery trials was to compare celery grown in areas that are wetter due to subsidence and celery grown in an upper area that has been less impacted by subsidence. In addition, over the same period, monitoring was done of two compost piles: one older pile that has been actively used and maintained for a few years that will no longer be maintained (i.e. adding of new material for decomposition) and the establishment of a new compost pile. The monitoring of the compost pile is part of a larger effort to determine the thermal impact of commonly used agricultural practices and the potential impact on permafrost. 
                        more » 
                        « less   
                    
                            
                            Temperature monitoring of various crop with and without seasonal extension techniques during the 2022 growing season in Fairbanks, Alaska
                        
                    
    
            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). 
        more » 
        « less   
        
    
                            - Award ID(s):
- 2126965
- PAR ID:
- 10498468
- Publisher / Repository:
- NSF Arctic Data Center
- Date Published:
- Subject(s) / Keyword(s):
- agriculture ground temperature permafrost crop extension techniques
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
- 
            
- 
            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.more » « less
- 
            Abstract. Agriculture plays a major role in eradicating poverty, promoting prosperity, and nourishing a projected 10 billion people by 2050 globally. In a changing climate, achieving optimal agricultural yields requires a deeper understanding of available natural resources and crops. This is especially important for places like the Navajo Nation, which faces significant challenges in food supply chain management due to various factors such as water demand, water quality, and insufficient information about land fertility and crops timings/seasons. Additionally, it is the largest Native American reservation in the U.S. It covers 27,425 square miles across Arizona, Utah, and New Mexico and has a population of 165,158 people, according to the 2020 census. Agriculture has been a key part of life in the Navajo Nation since the late 19th and early 20th centuries, playing a big role in the region’s development and stability. However, the lack of knowledge about decisions and actions during the crop growing season has resulted in lower crop productivity, as evidenced by the USDA statistical report for the Navajo Nation in 2012 and 2017. To support farmers by providing better decision-making and actionable insights, high-resolution, open-source Sentinel-2 satellite images are being used to develop advanced crop mapping techniques for identifying the spatial extent of various agricultural crops in the Navajo Nation. To address this, a collection of research papers was reviewed, leading to the development of a new methodology for analysing Sentinel-2 data from the 2017 and 2023 growing seasons within the Navajo Nation. The collected data was pre-processed by creating monthly median composites of surface reflectance to remove noise and enhance the results more accurately. After preprocessing, spectral indices were calculated from the spectral bands, including NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), GCVI (Green Chlorophyll Vegetation Index), and LSWI (Land Surface Water Index), to differentiate the crops more precisely. The training datasets were obtained from the USDA’s Crop Data Layer (CDL) and split into 80% for training and 20% for validating the Random Forest supervised classification algorithm. The classification resulted in an accuracy of 80%. Finally, the accuracy of the results was compared with independent ground truth data. This research identifies notable discrepancies between the CDL data and the Navajo Nation agricultural census statistical report, particularly in estimating corn acreage for the Chinle and Fort Defiance agencies. Ultimately this approach information is used to provide actionable insights to Navajo Nation farmers.more » « less
- 
            For farmers, policymakers, and government agencies, it is critical to accurately define agricultural crop phenology and its spatial-temporal variability. At the moment, two approaches are utilized to report crop phenology. On one hand, land surface phenology provides information about the overall trend, whereas weekly reports from USDA-NASS provide information about the development of particular crops at the regional level. High-cadence earth observations might help to improve the accuracy of these estimations and bring more precise crop phenology classifications closer to what farmers demand. The second component of the proposed solution requires the use of robust classifiers (e.g., random forest, RF) capable of successfully managing large data sets. To evaluate this solution, this study compared the output of a RF classifier model using weather, two different satellite sources (Planet Fusion; PF and Sentinel-2; S-2), and ground truth data to improve maize (Zea mays L.) crop phenology classification using two regions of Kansas (Southwest and Central) as a testbed during the 2017 growing season. Our findings suggests that high temporal resolution (PF) data can significantly improve crop classification metrics (f1-score = 0.94) relative to S-2 (f1-score = 0.86). Additionally, a decline in the f1-score between 0.74 and 0.60 was obtained when we assessed the ability of S-2 to extend the temporal forecast for crop phenology. This research highlights the critical nature of very high temporal resolution (daily) earth observation data for crop monitoring and decision making in agriculture.more » « less
- 
            The Midnight Sun Golf Course in Fairbanks, Alaska is a legacy farm field that is part of the National Science Foundation (NSF) Funded Permafrost Grown project. This 65 hectare (ha) parcel was initially cleared for agriculture purposes but changed land-use practices to a golf course around 25 years ago. The land-use conversion was in part due to ice-rich permafrost thaw following clearing. We are studying the long-term effects of permafrost thaw following initial clearing for cultivation purposes. We are working with the current landowners to provide information regarding ongoing thermokarst development on the property and to conduct studies in reforested portions of the land area to understand land clearing and reforestation on permafrost-affected soils. In this regard, we have acquired very high resolution light detection and ranging (LiDAR) data and digital photography from a DJI M300 drone using a Zenmuse L1. The Zenmuse L1 integrates a Livox Lidar module, a high-accuracy inertial measurement units (IMU), and a camera with a 1-inch CMOS on a 3-axis stabilized gimbal. The drone was configured to fly in real-time kinematic (RTK) mode at an altitude of 60 meters above ground level using the DJI D-RTK 2 base station. Data was acquired using a 50% sidelap and a 70% frontlap. Additional ground control was established with a Leica GS18 global navigation satellite system (GNSS) and all data have been post-processed to World Geodetic System 1984 (WGS84) universal transverse mercator (UTM) Zone 6 North using ellipsoid heights. Data outputs include a two-class classified LiDAR point cloud, digital surface model, digital terrain model, and an orthophoto mosaic. Image acquisition occurred on 10 September 2023. The input images are available for download at http://arcticdata.io/data/10.18739/A2PC2TB1T.more » « less
 An official website of the United States government
An official website of the United States government 
				
			 
					 
					
