master_table_lit_search_analysis_basic_155_models_20230225.xlsx

This spreadsheet was produced following a literature search conducted across the Scopus, EBSCOhost, and Web of Science (WoS) databases. The search targeted peer-reviewed papers, books, and book chapters published globally from January 2015 to December 2021. The search string 
"(estimat* OR predict*) AND (yield* OR product*) AND (wheat OR barley OR canola OR oilseed* OR rapeseed* OR cereal*) AND model* "
was applied across the three databases and a separate EndNote data base accumulated over the period 01/01/2020 – 31/12/2021 using ad hoc searches and publisher alerts.   
The Scopus data base was searched first, followed by searches across the EBSCOhost, WoS and EndNote databases in that order. After duplicate records were removed, search results were filtered using the following criteria:

The paper, book chapter or book was peer reviewed and written in English
The primary purpose of the paper, book chapter or book was the development and testing of a yield prediction model
The location, crop, yield units, input and prediction scale and extent were thoroughly documented
The models targeted either wheat, barley, or canola (or some combination of these crops) 
The model output resolution was at sub-paddock scale or higher (e.g. paddock regional, national), but excluded plot scale models
The model was applied across extensive areas (> 100km x 100km)
Input data (for running the model) was limited to accessible published data. Model development and calibration may use in situ data, but the model must not rely on in situ data to run. In this study, the data typically included publicly available, satellite-based, remotely sensed data and spatially continuous meteorological, soil moisture, landform, soil, and agronomic data as well as crop maps. Data collected by, UAV or other airborne sensors, paddock scale in situ measurements or private farmer records, were excluded. 

The search identified a total of 11,908 papers. After removing duplicate records and filtering based on title and the abstract, this was reduced to 388 papers. Another 46 papers were identified and added from the EndNote database. After a detailed scrutiny of the papers and strict application of the search criteria, 118 papers were identified that fitted the literature review criteria. Some of these papers described models for multiple crop types and a range of spatial resolution. This resulted in the review identifying 155 models.

本电子表格的编制基于对 Scopus、EBSCOhost 和 Web of Science (WoS) 数据库的文献检索。检索范围涵盖了全球范围内从2015年1月至2021年12月发表的经过同行评审的论文、书籍及书籍章节。检索字符串“(estimat* OR predict*) AND (yield* OR product*) AND (wheat OR barley OR canola OR oilseed* OR rapeseed* OR cereal*) AND model*”被应用于这三个数据库，并在2020年1月1日至2021年12月31日期间，通过定制搜索和出版商警报，独立地构建了一个EndNote数据库。首先检索了Scopus数据库，随后按照顺序检索了EBSCOhost、WoS和EndNote数据库。在移除重复记录后，通过以下标准对检索结果进行了筛选：

- 论文、书籍章节或书籍系英文撰写且经过同行评审
- 论文、书籍章节或书籍的主要目的是开发与测试产量预测模型
- 详细记录了地理位置、作物、产量单位、输入和预测的尺度与范围
- 模型针对的作物为小麦、大麦或油菜（或这些作物的组合）
- 模型输出的分辨率达到子围栏尺度或更高（例如围栏区域、国家尺度），但排除了围栏尺度模型
- 模型在广阔的区域（>100km x 100km）中得到应用
- 运行模型所需的数据仅限于可获取的已发表数据。模型开发和校准可能使用现场数据，但模型不得依赖于现场数据运行。在本研究中，数据通常包括公开可用的、基于卫星的遥感数据，以及空间连续的气象、土壤湿度、地形、土壤和农业数据，以及作物图。由无人机或其他航空传感器收集的数据、围栏尺度的现场测量或私人农民记录被排除在外。

检索共识别出11,908篇论文。在去除重复记录并基于标题和摘要进行筛选后，数量减少至388篇。另外，从EndNote数据库中又确定了46篇论文并添加进来。经过对论文的详细审查和严格遵循搜索标准，共确定了118篇符合文献综述标准的论文。其中一些论文描述了针对多种作物类型和不同空间分辨率的模型。这导致综述中识别出155个模型。