Soil and Climate Characterization to Define Environments for Summer Crops in Senegal

Investigating soil and climate variability is critical to defining environments for field crops, understanding yield-limiting factors, and contributing to the sustainability and resilience of agro-ecosystems. Following this rationale, the aim of this study was to develop a soil–climate characterization to describe environmental constraints in the Senegal summer-crops region. For the soil database, 825 soil samples were collected characterizing pH, electrical conductivity (EC), phosphorus (P), potassium (K), cation exchange capacity (CEC), and total carbon (C) and nitrogen (N). For the climate, monthly temperature, precipitation, and evapotranspiration layers were retrieved from WorldClim 2.1, CHIRPS and TERRACLIMATE. The same analysis was applied individually to both databases. Briefly, a principal component analysis (PCA) was executed to summarize the spatial variability. The outcomes from the PCA were subjected to a spatial fuzzy c-means algorithm, delineating five soil and three climate homogeneous areas, accounting for 73% of the soil and 88% of the climate variation. To our knowledge, no previous studies were done with large soil databases since availability field data is often limited. The use of soil and climate data allowed the characterization of different areas and their main drivers. The use of this classification will assist in developing strategic planning for future land use and capability classifications.

A Comparative Study of Various Methods of Handling Missing Data in UNSODA

UNSODA, a free international soil database, is very popular and has been used in many fields. However, missing soil property data have limited the utility of this dataset, especially for data-driven models. Here, three machine learning-based methods, i.e., random forest (RF) regression, support vector (SVR) regression, and artificial neural network (ANN) regression, and two statistics-based methods, i.e., mean and multiple imputation (MI), were used to impute the missing soil property data, including pH, saturated hydraulic conductivity (SHC), organic matter content (OMC), porosity (PO), and particle density (PD). The missing upper depths (DU) and lower depths (DL) for the sampling locations were also imputed. Before imputing the missing values in UNSODA, a missing value simulation was performed and evaluated quantitatively. Next, nonparametric tests and multiple linear regression were performed to qualitatively evaluate the reliability of these five imputation methods. Results showed that RMSEs and MAEs of all features fluctuated within acceptable ranges. RF imputation and MI presented the lowest RMSEs and MAEs; both methods are good at explaining the variability of data. The standard error, coefficient of variance, and standard deviation decreased significantly after imputation, and there were no significant differences before and after imputation. Together, DU, pH, SHC, OMC, PO, and PD explained 91.0%, 63.9%, 88.5%, 59.4%, and 90.2% of the variation in BD using RF, SVR, ANN, mean, and MI, respectively; and this value was 99.8% when missing values were discarded. This study suggests that the RF and MI methods may be better for imputing the missing data in UNSODA.

Validating the regional estimates of changes in soil organic carbon by using the data from paired-sites: the case study of Mediterranean arable lands

Background Legacy data are unique occasions for estimating soil organic carbon (SOC) concentration changes and spatial variability, but their use showed limitations due to the sampling schemes adopted and improvements may be needed in the analysis methodologies. When SOC changes is estimated with legacy data, the use of soil samples collected in different plots (i.e., non-paired data) may lead to biased results. In the present work, N = 302 georeferenced soil samples were selected from a regional (Sicily, south of Italy) soil database. An operational sampling approach was developed to spot SOC concentration changes from 1994 to 2017 in the same plots at the 0–30 cm soil depth and tested. Results The measurements were conducted after computing the minimum number of samples needed to have a reliable estimate of SOC variation after 23 years. By applying an effect size based methodology, 30 out of 302 sites were resampled in 2017 to achieve a power of 80%, and an α = 0.05. A Wilcoxon test applied to the variation of SOC from 1994 to 2017 suggested that there was not a statistical difference in SOC concentration after 23 years (Z = − 0.556; 2-tailed asymptotic significance = 0.578). In particular, only 40% of resampled sites showed a higher SOC concentration than in 2017. Conclusions This finding contrasts with a previous SOC concentration increase that was found in 2008 (75.8% increase when estimated as differences of 2 models built with non-paired data), when compared to 1994 observed data (Z = − 9.119; 2-tailed asymptotic significance < 0.001). This suggests that the use of legacy data to estimate SOC concentration dynamics requires soil resampling in the same locations to overcome the stochastic model errors. Further experiment is needed to identify the percentage of the sites to resample in order to align two legacy datasets in the same area.

USING SPATIAL DATA TO SIMULATE RAIN RUNOFF

The paper presents the main stages of spatial data preparation for computer models of runoff formation by the example of the Charysh River basin. DEM was employed to construct a system of sub-basins and a hydrological graph as well as to calculate morphometric characteristics of its elements. Sources of data on vegetation and mechanical composition of soils are given Using the Harmonized World Soil Database and the soil map of Altai Krai, a map of soil texture (grain-size composition) of the Charysh River basin was created. Two ways of precipitation accounting (the weather station data; the Persiann-CDR data set) were compared. Calculations of runoff from the the Charysh catchment were made for two models.

A Comparison, Validation, and Evaluation of the S-world Global Soil Property Database

Despite the increased usage of global soil property maps, a proper review of the maps rarely takes place. This study aims to explore the options for such a review with an application for the S-World global soil property database. Global soil organic carbon (SOC) and clay content maps from S-World were studied at two spatial resolutions in three steps. First, a comparative analysis with an ensemble of seven datasets derived from five other global soil databases was done. Second, a validation of S-World was done with independent soil observations from the WoSIS soil profile database. Third, a methodological evaluation of S-world took place by looking at the variation of soil properties per soil type and short distance variability. In the comparative analysis, S-World and the ensemble of other maps show similar spatial patterns. However, the ensemble locally shows large discrepancies (e.g., in boreal regions where typically SOC contents are high and the sampling density is low). Overall, the results show that S-World is not deviating strongly from the model ensemble (91% of the area falls within a 1.5% SOC range in the topsoil). The validation with the WoSIS database showed that S-World was able to capture a large part of the variation (with, e.g., a root mean square difference of 1.7% for SOC in the topsoil and a mean difference of 1.2%). Finally, the methodological evaluation revealed that estimates of the ranges of soil properties for the different soil types can be improved by using the larger WoSIS database. It is concluded that the review through the comparison, validation, and evaluation provides a good overview of the strengths and the weaknesses of S-World. The three approaches to review the database each provide specific insights regarding the quality of the database. Specific evaluation criteria for an application will determine whether S-World is a suitable soil database for use in global environmental studies.

Improving the global modeling of soils in JULES and the Unified Model: Updating from UM/HWSD to SoilGrids soil properties and from the Brooks & Corey to the van Genuchten soil-hydraulics model

&lt;p&gt;&amp;#160;&amp;#160;&amp;#160; We have updated the soil properties used in JULES (Joint UK Land Environment Simulator), which is the land-surface component of the UM (Unified Model, the UK Met Office&amp;#8217;s climate model). JULES models the interaction of the land surface with the atmosphere, and simulates the energy, water, and carbon fluxes. JULES allows either: (i) the Brooks &amp; Corey (BC) model for estimating soil hydraulic properties, or (ii) the van Genuchten (VG) model but using hydraulic parameters translated from the BC model. One advantage of the VG model over the BC model is the smoother dependence of water retention upon matric potential for nearly saturated soils. Herein, we report on our work towards fully implementing the VG model in JULES and in the UM, through the implementation and evaluation of several VG pedotransfer functions (PTFs) for estimating the soil hydraulic parameters used in the hydraulic functions.&lt;/p&gt; &lt;p&gt;&amp;#160;&amp;#160;&amp;#160; We have tested three VG PTFs in global offline JULES runs (driven with WFDEI data over 1979-2012): the combination of T&amp;#243;th et al. PTFs 17 &amp; 20, the Weynants et al. PTF, and the Zhang &amp; Schaap ROSETTA3 H1 PTF (modified for sandy soils). We also modernized the soil basic properties that are conventionally used for JULES and the UM, from the UM version of the Harmonized World Soil Database (HWSD) to the SoilGrids database.&lt;/p&gt; &lt;p&gt;&amp;#160;&amp;#160;&amp;#160; Evaluation of JULES simulations shows (i) that the modified version of the Zhang &amp; Schaap ROSETTA3 H1 PTF is the best VG option, and (ii) that it compares favorably with the BC control model (which uses the Cosby et al. PTF and the UM/HWSD soils), in terms of the surface energy balance and the mitigation of near-surface temperature biases over mid-latitude continental regions. This modified version of the Zhang &amp; Schaap ROSETTA3 H1 PTF with SoilGrids soils is also currently being used in coupled land-atmosphere UM runs.&lt;/p&gt;

Spreading herbivore manure in livestock farms increases soil carbon content, while granivore manure decreases it

Livestock farming occupies 57% of agricultural area in France and has contrasting impacts on the environment. Studies have analyzed relations between livestock farming and soil organic carbon (SOC) content, but the influence of livestock farming on soils is difficult to perceive at a large scale. The objective of this study was to increase understanding of impacts of livestock farming on soils that receive livestock manure depending on different initial levels of SOC content, at cantonal level. To this end, we used French soil and agricultural databases to analyze relations between livestock farming practices and SOC content. We used statistical data calculated from the French soil test database for the periods 2000–2004 and 2010–2014. For livestock farming practices, we used data from the French agricultural census of 2000 and 2010, and for spreading of livestock manure, data from the French program to control pollution of agricultural origin (2002–2007) and data from the French Livestock Institute. The novelty of our large-scale analysis is to differentiate the origin of livestock manure (herbivore or granivore) and the type of crop on which it was spread (crops or grasslands). Statistical analysis was performed at the cantonal scale for France using the method of generalized least squares. We show for the first time that, at the national scale, spreading of livestock manure influences SOC content and dynamics significantly. Our results also show the importance of the nature of the manure; solid manure increases SOC content, unlike liquid manure. Spreading herbivore manure on crops increases SOC content, but spreading granivore manure may decrease it. Livestock manure spread on grasslands has no significant effect on SOC content, possibly due to under-representation of grassland soils in the soil database. These results demonstrate the importance of the complementary between crop and livestock to maintain soil ecosystem services, including soil fertility.