GC035
Evolving Aspects of Agricultural Monitoring Systems
Session ID#: 78625
Session Description:
Deep knowledge at multiple scales about agricultural systems is needed by practitioners, governments, and businesses to address risk, build resiliency, and measure change. Remote sensing, GIS science, machine learning, time-series analysis, and modeling framework methods help derive accurate, geospatially-driven assessments of agricultural processes and systems on crop-, pasture-, rangelands, and urban agriculture. This session features research methods, tools, and applications in monitoring, mapping, and quantifying change in agricultural systems. Topics include sustainability, climate, resilience, risk reduction practices, index insurance and economic modeling, ag-econometrics, agroecology, conservation and/or climate-smart agriculture, land use/land cover conversions, phenology, crop diversification, rotation and intensification, renewable agriculture, urban farming, food security, food and land sovereignty for monitoring agricultural area, condition, productivity, and yield.
Index Terms:
0402 Agricultural systems [BIOGEOSCIENCES]
1630 Impacts of global change [GLOBAL CHANGE]
1632 Land cover change [GLOBAL CHANGE]
1640 Remote sensing [GLOBAL CHANGE]
Primary Convener: Rick Mueller, USDA NASS, Spatial Analysis Research Section, Fairfax, VA, United States and Jessica McCarty; Jesslyn Brown; Caitlin Kontgis
Conveners: Jessica L McCarty, Miami University Oxford, Oxford, OH, United States, Caitlin Kontgis, UW-Madison, Madison, WI, United States, Jesslyn F Brown, USGS/EROS, Sioux Falls, SD, United States and Jessica McCarty; Jesslyn Brown; Caitlin Kontgis
Primary Liaison: Rick Mueller, USDA Washington DC, Washington, DC, United States and Jessica McCarty; Jesslyn Brown; Caitlin Kontgis
Field scale crop calendars for soybean agriculture in Mato Grosso, Brazil (489665)
Minghui Zhang, University of California Berkeley, Berkeley, CA, United States and Sally E Thompson, University of California, Berkeley, Department of Civil and Environmental Engineering, Berkeley, CA, United States
A grid-based spatial clustering method for monitoring the cultivated land quality in China at the national scale (489840)
Cong Ou1, Jianyu Yang1, Zhenrong Du1, Bingbo Gao1, Dehai Zhu1, Zhenbo Du1 and Key Laboratory for Agricultural Land Quality Monitoring and Control, Ministry of Natural Resources, P.R.C, (1)CAU China Agricultural University, College of Land Science and Technology, Beijing, China
Agricultural monitoring from a humanitarian perspective - Understanding links between hazards, impacts and novel financial instruments (Invited) (491546)
Markus Enenkel, Harvard University, Cambridge, MA, United States and Patrick Vinck, Harvard University, Harvard Humanitarian Initiative, Cambridge, MA, United States
Fertilization effects on the long-term stability of crop yields (492205)
Hella Ellen Ahrends1, Thomas K Gaiser2, Stefan Siebert3, Frank Ewert2, Werner Eugster4, Sabine Seidel1, Hubert Hüging1, Thomas Döring1 and Ehsan Eyshi Rezaei5, (1)University of Bonn, Bonn, Germany, (2)University of Bonn, Institute of Crop Science and Resource Conservation (INRES), Bonn, Germany, (3)University of Göttingen, Institute for Crop Science, Göttingen, Germany, (4)ETH Zurich, Zurich, Switzerland, (5)University of Göttingen, Göttingen, Germany
Developing Improved Leaf Wetness Duration Models with Machine Learning and Climate Reanalysis Data (498835)
Parisa Asadi, PhD Student, Biosystem engineering, Auburn, AL, United States and Di Tian, Auburn University, Crop, Soil, and Environmental Science, Auburn, AL, United States
Select Maps from the 2017 Census of Agriculture (501724)
Rick Mueller1, Avery Sandborn2 and Lee Ebinger1, (1)USDA Washington DC, Washington, DC, United States, (2)USDA National Agricultural Statistics Service, Washington, DC, United States
Insights into Effective Satellite Crop Yield Estimation from an Extensive Ground Truth Dataset in the US Corn Belt (Invited) (526052)
Jillian M Deines1, Walter Dado2, Rinkal Patel3 and David B Lobell2, (1)Stanford University, Department of Earth System Science, Stanford, CA, United States, (2)Stanford University, Stanford, CA, United States, (3)Granular, Inc., San Francisco, DC, United States
Mapping Crop Emergence in Near Real-time at Field Scales Using High Temporal and Spatial Resolution Imagery: Challenges and Opportunities (542150)
Feng Gao1, Martha Anderson1, Craig S.T. Daughtry2 and William P Kustas3, (1)USDA ARS Hydrology and Remote Sensing Laboratory, Beltsville, MD, United States, (2)USDA ARS, Hydrology & Remote Sensing Lab, Beltsville, MD, United States, (3)U. S. Department of Agriculture, Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville, MD, United States
Developing Predictive Risk Models for Pest Outbreaks using Remote Sensing Data, Weather Information and Farmer-provided Reports on Crop Damage (579602)
Molly Elizabeth Brown1,2, Vladimir Eskin3 and Jyothy Nagol3, (1)University of Maryland College Park, Department of Geographical Sciences, College Park, MD, United States, (2)6th Grain Corporation, Bethesda, MD, United States, (3)6th Grain Corporation, Bethesda, United States
Evaluation of soil-dependent crop yield outcomes in Nepal using ground and satellite-based approaches (598740)
Jake Campolo1, David B Lobell1, David T Güereña2 and Shashish Maharjan3, (1)Stanford University, Stanford, CA, United States, (2)International Maize and Wheat Improvement Center (CIMMYT), Mexico City, Mexico, (3)International Maize and Wheat Improvement Center (CIMMYT), Kathmandu, Nepal
Improving satellite-based soybean yield mapping across irrigated and rain-fed conditions (609480)
Walter Dado1, Jillian M Deines2 and David B Lobell1, (1)Stanford University, Stanford, CA, United States, (2)Michigan State University, Department of Earth and Environmental Sciences, East Lansing, MI, United States
Remote sensing of crop residue using the SWIR bands of the Worldview-3 satellite platform (612643)
W. D. Hively, USGS Eastern Geographic Science Center, Reston, VA, United States, Brian Thomas Lamb, CUNY City College, New York, NY, United States, Craig S.T. Daughtry, USDA ARS, Hydrology & Remote Sensing Lab, Beltsville, MD, United States and Miguel Quemada, Universidad Politécnica de Madrid, CEIGRAM/Agricultural Production, Madrid, Spain
A Deep Learning Approach to Estimate Plant Density and Spread: Application to Durum Wheat and Sorghum Crops (614141)
Aishanjiang Yasen, Saint Louis University, Earth and Atmospheric Sciences, Saint Louis, United States, Vasit Sagan, Saint Louis University, Earth and Atmospheric Sciences, St. Louis, MO, United States, Sidike Paheding, Saint Louis University, Earth and Atmospheric Sciences, Saint Louis, MO, United States, Maitiniyazi Maimaitijiang, Maria Newcomb, University of Arizona, School of Plant Sciences, Maricopa, AZ, United States, David LeBauer, Energy Biosciences Institute, Urbana, IL, United States, Nadia Shakoor, Danforth Center, Saint Louis, United States and Todd Mockler, Danforth Center, Saint Louis, MO, United States
Is a Picture Worth a Thousand Measurements? A Comparison of Drone Systems and Data Processing Methods for Rangeland Vegetation Monitoring (618030)
Chandra Holifield Collins1, Susan M Skirvin2, Gerardo Armendariz1, Jeffrey Gillan3, Jason Wong1, Guillermo Ponce-Campos2, Tyson Lee Swetnam4, Alisa Coffin5, Zachary Winston3, Andrew Corrales3, Philip Heilman6 and Loretta Metz7, (1)USDA-ARS Southwest Watershed Research Center, Tucson, AZ, United States, (2)University of Arizona, Tucson, AZ, United States, (3)University of Arizona, Tucson, United States, (4)University of Arizona, BIO5 Institute, Tucson, AZ, United States, (5)USDA, ARS, Tifton, GA, United States, (6)USDA-ARS Southwest Watershed Research Center, Tucson, United States, (7)USDA-Natural Resources Conservation Service, Tucson, United States
Asymmetric cropland losses shaped by city hierarchy in China (620538)
Bingwen Qiu1, Zhenghong Tang2, Haiwen Li Mr.3, Chongcheng Chen3 and Joe A Berry4, (1)Palo Alto, CA, United States, (2)University of Nebraska Lincoln, School of Natural Resources, Lincoln, NE, United States, (3)Fuzhou University, Fuzhou, China, (4)Carnegie Institution for Science Stanford, Stanford, CA, United States
