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

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

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

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   https://doi.org/10.1016/j.agsy.2022.103462  

   Zhang, Chen; Di, Liping; Lin, Li; Li, Hui; Guo, Liying; Yang, Zhengwei; Yu, Eugene G.; Di, Yahui; Yang, Anna (August 2022, Agricultural Systems)