DOI: 10.15482/usda.adc/31048804.v1
title: Data from: Simulated grain aeration at food facilities in the desert biomes of the Middle East: Grain aeration in environments close to arthropod upper developmental threshold are ineffective

description: <i>Weather Data Compilation and Site Selection</i>Historical weather data from 2004–2023 was obtained for 77 sites in 13 countries in the Middle East using the US National Centers for Environmental Information past weather database (Supplementary Table 1) (USNCEI 2025). In cases where data was missing, it was interpolated between maximum and minimum (or both) values before and after the missing dates. The maximum and minimum temperatures for each date and location were analyzed to determine the number of hours below 15, 18, and 21 °C on each date. The hours below each threshold were summed by month. A degree-day model method has been used for pests of asparagus (Morrison et al. 2014), and in other aeration studies for stored product insects (Arthur et al. 2020, Morrison et al. 2020, Morrison et al. 2022). In short, the model calculates a daily sine curve based on the maximum and minimum daily temperatures to estimate the hourly temperature and the number of hours below the specified aeration threshold.<i>Spatial Interpolation of Hours below Thresholds from Historical Weather Data</i>Accumulated daily hours below 15, 18, and 21°C were calculated for each location between July, August, September, October, and November, and were then used to spatially interpolate to geographic information using latitude and longitude (at a spatial scale of 0.001 degrees) to locations in the Middle East for hours below each threshold for each of these months using QGIS v.3.12.3 (QGIS.org 2022). The SAGA processing tool in the QGIS software was used to implement inverse distance weighted (e.g., square root) interpolation of hours between the point information using a cell raster size of 0.01 × 0.01 pixels, using the projection coordinate system EPSG:4326-WGS 84. The process was also used to spatially interpolate the predicted <i>S. oryzae </i>population (see the model below). The assumption used to evaluate scenarios is that aeration would be activated via an automated controller when the average grain temperature inside a storage bin is above the threshold and the ambient air temperature is lower than the average grain temperature and the ambient relative humidity can maintain equilibrium grain moisture (Yang et al. 2017). The airflow is a front of cool air drawn from the bottom of the grain mass, moving upwards through the grain until the entire mass reaches equilibrium. Consequently, there would be no cooling when ambient temperatures are higher than grain temperatures.<i>Modelling S. oryzae Populations</i>The Post-Harvest Grain Management program (https://etools.beaumont.tamu.edu/GrainManagement/) is a web-based grain management tool that allows users to develop strategies to optimize the control of <i>S. oryzae</i>, <i>R. dominica</i>, and red flour beetle, <i>Tribolium castaneum</i> (Herbst) (Coleoptera: Tenebrionidae), in rice storage bins and in rice mills. The model provides an interactive web-powered interface for stakeholders to customize storage and mill configurations and to evaluate the effectiveness of different pest control options. This model is linked to a weather database that contains up-to-date data for the six major rice-growing states (Arkansas, California, Louisiana, Mississippi, Missouri and Texas) in the US. Stakeholders also have the option to add their own weather data to meet their specific simulation and analysis needs. The post-harvest model was developed by (Yang et al. 2017, Yang and Wilson 2025). See Yang et al. (2017) for details on the model structure. The model can be configured to start aeration immediately after grain is binned, on any date thereafter, with or without using a specified threshold temperature for an aeration cycle. This model assumes that the number of grains is not a limiting factor for insect population increase, which is generally true.A subset of 10 locations was used in the Middle East for modeling <i>S. oryzae</i> populations to ensure that the range of climatic conditions were represented as defined by the Köppen-Geiger climate classification system (Peel et al. 2007; Table 1). The composite weather data for each station were imported into the Post-Harvest Grain Management program through its web interface and used to predict <i>S. oryzae </i>populations for each combination of location, aeration threshold (no aeration, 15, 18, and 21°C), and month of beginning of aeration (first of each month: July, August, September, October, and December) with aeration ending on May 31 the following year for all aeration scenarios. The temperature of the wheat at time of storage and binning was held constant at 32°C, regardless of location or climatic zone for several reasons. The Middle East is a heterogeneous region that would have resulted in too many combinations if the starting grain temperature along with aeration thresholds and beginning of aeration were modified, and it would have made it difficult to compare results for locations in the same region. Wheat cultivated in a warmer region of a country could be transported for storage at a milling site located in a different region. Moreover, small grain harvest takes place earlier, as compared with that in northern areas of East Mediterranean, such as the Balkan Peninsula (Morrison et al. 2020). Finally, we had no reliable basis or data on which to alter the storage temperature. Taken together, this variability could have led to speculation or criticism regarding the alteration of storage temperature for the purpose of this modeling comparison study. Initial <i>S. oryzae</i> population was set at 0.75 adults/MT (metric ton). Rice is less dense than wheat, so the model may overestimate the effects of aeration. However, there is no other web-based model that could be used for simulation studies at the current time.Locations were grouped <i>a posteriori</i> into areas where grain aeration is considered ineffective and effective. This was based on whether populations of <i>S. oryzae</i> in aerated grain were about equal (ineffective) or suppressed (effective) compared to grain masses without aeration by the end of the simulation under different triggering thresholds.<br>