CropPol: a dynamic, open and global database on crop pollination

This is the final version. Available from Wiley via the DOI in this record The original dataset (v1.1.0) of the CropPol database can be accessed from the ECOLOGY repository. Main upgrades of these datasets will be versioned and deposited in Zenodo (DOI: 10.5281/zenodo.5546600)Data availability. V.C. Computer programs and data-processing algorithms: The algorithms used in deriving, processing, or transforming data can be accessed in the DataS1.zip file and the Zenodo repository (DOI: 10.5281/zenodo.5546600). V.D. Archiving: The data is archived for long-term storage and access in Zenodo (DOI: 10.5281/zenodo.5546600)Seventy five percent of the world's food crops benefit from insect pollination. Hence, there has been increased interest in how global change drivers impact this critical ecosystem service. Because standardized data on crop pollination are rarely available, we are limited in our capacity to understand the variation in pollination benefits to crop yield, as well as to anticipate changes in this service, develop predictions, and inform management actions. Here, we present CropPol, a dynamic, open and global database on crop pollination. It contains measurements recorded from 202 crop studies, covering 3,394 field observations, 2,552 yield measurements (i.e. berry weight, number of fruits and kg per hectare, among others), and 47,752 insect records from 48 commercial crops distributed around the globe. CropPol comprises 32 of the 87 leading global crops and commodities that are pollinator dependent. Malus domestica is the most represented crop (32 studies), followed by Brassica napus (22 studies), Vaccinium corymbosum (13 studies), and Citrullus lanatus (12 studies). The most abundant pollinator guilds recorded are honey bees (34.22% counts), bumblebees (19.19%), flies other than Syrphidae and Bombyliidae (13.18%), other wild bees (13.13%), beetles (10.97%), Syrphidae (4.87%), and Bombyliidae (0.05%). Locations comprise 34 countries distributed among Europe (76 studies), Northern America (60), Latin America and the Caribbean (29), Asia (20), Oceania (10), and Africa (7). Sampling spans three decades and is concentrated on 2001-05 (21 studies), 2006-10 (40), 2011-15 (88), and 2016-20 (50). This is the most comprehensive open global data set on measurements of crop flower visitors, crop pollinators and pollination to date, and we encourage researchers to add more datasets to this database in the future. This data set is released for non-commercial use only. Credits should be given to this paper (i.e., proper citation), and the products generated with this database should be shared under the same license terms (CC BY-NC-SA). This article is protected by copyright. All rights reserved.OBServ Projec

Soil moisture prediction of bare soil profiles using diffuse spectral reflectance information and vadose zone flow modeling

Soil hydraulic property information of the vadose zone is key to quantifying the temporal and spatial variability of soil moisture, and for modeling water flow and contaminant transport processes in the near surface. This study deals with exploring the feasibility of using diffuse soil spectral information in the visible, near-infrared and shortwave infrared range (350–2500 nm) to estimate coarse-scale soil hydraulic parameters and predict soil moisture profiles using a topography-based aggregation scheme in conjunction with a 1D mechanistic water flow model. Three different types of parametric transfer functions (so-called spectrotransfer functions, STFs; pedotransfer functions, PTFs; and spectral pedotransfer functions, SPTFs) were aggregated from the point scale to 1 km2 pixel size. to provide coarse scale estimates of van Genuchten-Mualem (VGM) hydraulic parameters. The coarse scale hydraulic parameters were evaluated by simulating soil water dynamics of the 1 km2 pixels across the Zanjanrood River sub-watershed (ZRS) in northwest Iran. Resultant soil water states were compared with ground-truth measurements and advanced synthetic aperture radar (ASAR) estimates of soil water content. The topography-based aggregation scheme was found to provide effective values of the VGM hydraulic parameters across the ZRS study site. The coarse scale STFs performed best in terms of simulating surface, near-surface and subsurface soil water dynamics, followed by the coarse scale SPTFs and PTFs, which performed similarly. The average simulated soil water contents of the surface layer closely correlated with ASAR estimates during relatively wet periods. Simulated subsurface soil water dynamics matched well with the ground-truth measurements. These findings indicate the feasibility of using spectral data to predict VGM hydraulic parameters and, ultimately, to predict soil water dynamics at the larger scales

A comparative study of multiple approaches for predicting the soil-water retention curve: Hyperspectral information vs. basic soil properties

Information about the soil-water retention curve is necessary for modeling water flow and solute transport processes in soils. Soil spectroscopy in the visible, nearinfrared, and shortwave infrared (Vis-NIR-SWIR) range has been widely used as a rapid, cost-effective and nondestructive technique to predict soil properties. However, less attention has been paid to predict soil hydraulic properties using soil spectral data. In this paper, spectral reflectances of soil samples from the Zanjanrood watershed, Iran, were measured in the Vis-NIR-SWIR ranges (350-2500 nm). Stepwise multiple linear regression coupled with the bootstrap method was used to construct predictive models and to estimate the soil-water retention curve. We developed point and parametric transfer functions based on the van Genuchten (VG) and Brooks-Corey (BC) soil hydraulic models. Three different types of transfer functions were developed: (i) spectral transfer functions (STFs) that relate VG/BC hydraulic parameters to spectral reflectance values, (ii) pedotransfer function (PTFs) that use basic soil data as input, and (iii) PTFs that consider spectral data and basic soil properties, further referred to as spectral pedotransfer functions (SPTFs). We also derived and evaluated point transfer functions which estimate soil-water contents at specific matric potentials. The point STFs and SPTFs were found to be accurate at low and intermediate water contents (R2 > 0.50 and root mean squared error [RMSE] <0.018 cm3 cm-3), while the point PTFs performed better close to saturation. The parametric STFs and SPTFs of both the VG and BC models performed similarly to parametric PTFs in estimating the retention curve. The best predictions of soil-water contents were obtained for all the three transfer functions when the VG and BC retention models were fitted to the retention points estimated by the point transfer functions. Overall, our findings indicate that spectral data can provide useful information to predict soil-water contents and the soil-water retention curve. However, there is a need to extend and validate the derived transfer functions to other soils and regions

A Comparative Study of Multiple Approaches for Predicting the Soil–Water Retention Curve: Hyperspectral Information vs. Basic Soil Properties

Information about the soil–water retention curve is necessary for modeling water flow and solute transport processes in soils. Soil spectroscopy in the visible, near-infrared, and shortwave infrared (Vis-NIR-SWIR) range has been widely used as a rapid, cost-effective and nondestructive technique to predict soil properties. However, less attention has been paid to predict soil hydraulic properties using soil spectral data. In this paper, spectral reflectances of soil samples from the Zanjanrood watershed, Iran, were measured in the Vis-NIR-SWIR ranges (350–2500 nm). Stepwise multiple linear regression coupled with the bootstrap method was used to construct predictive models and to estimate the soil–water retention curve. We developed point and parametric transfer functions based on the van Genuchten (VG) and Brooks-Corey (BC) soil hydraulic models. Three different types of transfer functions were developed: (i) spectral transfer functions (STFs) that relate VG/BC hydraulic parameters to spectral reflectance values, (ii) pedotransfer function (PTFs) that use basic soil data as input, and (iii) PTFs that consider spectral data and basic soil properties, further referred to as spectral pedotransfer functions (SPTFs). We also derived and evaluated point transfer functions which estimate soil–water contents at specific matric potentials. The point STFs and SPTFs were found to be accurate at low and intermediate water contents (R2 > 0.50 and root mean squared error [RMSE] < 0.018 cm3 cm−3), while the point PTFs performed better close to saturation. The parametric STFs and SPTFs of both the VG and BC models performed similarly to parametric PTFs in estimating the retention curve. The best predictions of soil–water contents were obtained for all the three transfer functions when the VG and BC retention models were fitted to the retention points estimated by the point transfer functions. Overall, our findings indicate that spectral data can provide useful information to predict soil—water contents and the soil–water retention curve. However, there is a need to extend and validate the derived transfer functions to other soils and regions

A comparative study of multiple approaches for predicting the soil-water retention curve: Hyperspectral information vs. basic soil properties

Information about the soil-water retention curve is necessary for modeling water flow and solute transport processes in soils. Soil spectroscopy in the visible, nearinfrared, and shortwave infrared (Vis-NIR-SWIR) range has been widely used as a rapid, cost-effective and nondestructive technique to predict soil properties. However, less attention has been paid to predict soil hydraulic properties using soil spectral data. In this paper, spectral reflectances of soil samples from the Zanjanrood watershed, Iran, were measured in the Vis-NIR-SWIR ranges (350-2500 nm). Stepwise multiple linear regression coupled with the bootstrap method was used to construct predictive models and to estimate the soil-water retention curve. We developed point and parametric transfer functions based on the van Genuchten (VG) and Brooks-Corey (BC) soil hydraulic models. Three different types of transfer functions were developed: (i) spectral transfer functions (STFs) that relate VG/BC hydraulic parameters to spectral reflectance values, (ii) pedotransfer function (PTFs) that use basic soil data as input, and (iii) PTFs that consider spectral data and basic soil properties, further referred to as spectral pedotransfer functions (SPTFs). We also derived and evaluated point transfer functions which estimate soil-water contents at specific matric potentials. The point STFs and SPTFs were found to be accurate at low and intermediate water contents (R2 > 0.50 and root mean squared error [RMSE] <0.018 cm3 cm-3), while the point PTFs performed better close to saturation. The parametric STFs and SPTFs of both the VG and BC models performed similarly to parametric PTFs in estimating the retention curve. The best predictions of soil-water contents were obtained for all the three transfer functions when the VG and BC retention models were fitted to the retention points estimated by the point transfer functions. Overall, our findings indicate that spectral data can provide useful information to predict soil-water contents and the soil-water retention curve. However, there is a need to extend and validate the derived transfer functions to other soils and regions