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1. Crop Field Boundary Delineation using Historical Crop Rotation Pattern

   https://doi.org/10.1109/Agro-Geoinformatics.2019.8820240  

   Rahman, Md. Shahinoor ; Di, Liping ; Yu, Zhiqi ; Yu, Eugene G. ; Tang, Junmei ; Lin, Li ; Zhang, Chen ; Gaigalas, Juozas ( July 2019 , 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics))

   GIS data layer on crop field boundary has many applications in agricultural research, ecosystem study, crop monitoring, and land management. Crop field boundary mapping through field survey is not time and cost effective for vast agriculture areas. Onscreen digitization on fine-resolution satellite image is also labor-intensive and error-prone. The recent development in image segmentation based on their spectral characteristics is promising for cropland boundary detection. However, processing of large volume multi-band satellite images often required high-performance computation systems. This study utilized crop rotation information for the delineation of field boundaries. In this study, crop field boundaries of Iowa in the United States are extracted using multi-year (2007-2018) CDL data. The process is simple compared to boundary extraction from multi-date remote sensing data. Although this process was unable to distinguish some adjacent fields, the overall accuracy is promising. Utilization of advanced geoprocessing algorithms and tools on polygon correction may improve the result significantly. Extracted field boundaries are validated by superimposing on fine resolution Google Earth images. The result shows that crop field boundaries can easily be extracted with reasonable accuracy using crop rotation information. 

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2. Global synthesis of cover crop impacts on main crop yield

   https://doi.org/10.1016/j.fcr.2024.109343  

   Peng, Yu ; Wang, Lixin ; Jacinthe, Pierre-André ; Ren, Wei ( April 2024 , Field Crops Research)
3. Cross-scale evaluation of dynamic crop growth in WRF and Noah-MP-Crop

   https://doi.org/10.1016/j.agrformet.2020.108217  

   Partridge, Trevor F. ; Winter, Jonathan M. ; Kendall, Anthony D. ; Hyndman, David W. ( January 2021 , Agricultural and Forest Meteorology)

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4. Using spatially variable nitrogen application and crop responses to evaluate crop nitrogen use efficiency

   https://doi.org/10.1007/s10705-023-10263-3  

   Hegedus, Paul B. ; Ewing, Stephanie A. ; Jones, Clain ; Maxwell, Bruce D. ( May 2023 , Nutrient Cycling in Agroecosystems)

   Abstract Low nitrogen use efficiency (NUE) is ubiquitous in agricultural systems, with mounting global scale consequences for both atmospheric aspects of climate and downstream ecosystems. Since NUE-related soil characteristics such as water holding capacity and organic matter are likely to vary at small scales (< 1 ha), understanding the influence of soil characteristics on NUE at the subfield scale (< 32 ha) could increase fertilizer NUE. Here, we quantify NUE in four conventionally managed dryland winter-wheat fields in Montana following multiple years of sub-field scale variation in experimental N fertilizer applications. To inform farmer decisions that incorporates NUE, we developed a generalizable model to predict subfield scale NUE by comparing six candidate models, using ecological and biogeochemical data gathered from open-source data repositories and from normal farm operations, including yield and protein monitoring data. While NUE varied across fields and years, efficiency was highest in areas of fields with low N availability from both fertilizer and estimated mineralization of soil organic N (SON). At low levels of applied N, distinct responses among fields suggest distinct capacities to supply non-fertilizer plant-available N, suggesting that mineralization supplies more available N in locations with higher total N, reducing efficiency for any applied rate. Comparing modelling approaches, a random forest regression model of NUE provided predictions with the least error relative to observed NUE. Subfield scale predictive models of NUE can help to optimize efficiency in agronomic systems, maximizing both economic net return and NUE, which provides a valuable approach for optimization of nitrogen fertilizer use. 

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5. Climate adaptation by crop migration

   https://doi.org/10.1038/s41467-020-15076-4  

   Sloat, Lindsey_L ; Davis, Steven_J ; Gerber, James_S ; Moore, Frances_C ; Ray, Deepak_K ; West, Paul_C ; Mueller, Nathaniel_D ( March 2020 , Nature Communications)

   Many studies have estimated the adverse effects of climate change on crop yields, however, this literature almost universally assumes a constant geographic distribution of crops in the future. Movement of growing areas to limit exposure to adverse climate conditions has been discussed as a theoretical adaptive response but has not previously been quantified or demonstrated at a global scale. Here, we assess how changes in rainfed crop area have already mediated growing season temperature trends for rainfed maize, wheat, rice, and soybean using spatially-explicit climate and crop area data from 1973 to 2012. Our results suggest that the most damaging impacts of warming on rainfed maize, wheat, and rice have been substantially moderated by the migration of these crops over time and the expansion of irrigation. However, continued migration may incur substantial environmental costs and will depend on socio-economic and political factors in addition to land suitability and climate.

    

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