Browsing by Subject "Geostatistics"

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Development of MidDRIFTS methodologies to support mapping of physico-chemical soil properties at the regional scale
(2014) Mirzaeitalarposhti, Reza; Müller, Torsten
Changing climate conditions and land-use change severely affect key ecosystem processes in soils. Hence, regular monitoring of essential soil properties are required to implement appropriate soil management in agro-ecosystems. However characterizing soil properties at different spatial scales remains challenging, requiring a large amount of geo-referenced data by intensive sampling. Mid-infrared diffuse reflectance infrared Fourier transform spectroscopy (midDRIFTS) in combination with partial least square regression (PLSR) was applied as a rapid-throughput method to quantify soil properties and to assess soil spatial patterns at the regional scale in two agro-ecological areas. A pre-sampling at the regional scale was done to develop the most efficient midDRIFTS-PLSR prediction models by testing two different calibration procedures, i.e. cross-validation and independent validation, to quantify essential soil properties with 126 sample points. A generic MidDRIFTS-PLSR prediction model was developed to predict most soil properties of “unknown” samples accurately using independent validation approach. The next step was the integration of midDRIFTS-PLSR with geostatistics to facilitate regional soil property mapping. Developed midDRIFTS-PLSR models were used to predict TC, TIC, TOC and soil texture contents (clay, silt and sand) of the 1170 soil samples. The midDRIFTS-PLSR models accurately predicted all soil properties. Furthermore, the integration of midDRIFTS-PLSR-based predicted data with geostatisitcs resulted in high resolution maps of soil carbon and texture at the regional scale which are an improvement over the existing maps. As a further development of midDRIFTS approaches for soil quality assessment, spectral-based indexes for characterizing SOM quality and quantifying carbonate at regional scale were explored. MidDRIFTS peak areas corresponding to SOM functional groups (2930, 1620, 1520 and 1159 cm-1) were assessed to study the composition of SOM. The peak assigned for aliphatic C-H bond (2930 cm-1) was an appropriate index to investigate SOM fractions if the interference of carbonates was taken into consideration. Regression performance obtained between the peak at 2930 cm-1 and SOM fractions (e.g., R2 = 0.31 for Cmic) increased to R2 = 0.65 when high carbonate containing samples (total inorganic carbon > 1%) were excluded. The most accurate spectral index for carbonate was the peak area at 713 cm-1 when relating to TIC obtained by Scheiblers method (R2 = 0.98). In conclusion, it was demonstrated that midDRIFTS-PLSR is a rapid-throughput method for providing high-quality predictions of soil properties to update regional digital soil property mapping by integration with geostatistics. It opens a new possibility to gain high resolution data coverage of soil C and N pools, which is relevant for the application of SOM simulation models on a regional scale. However, to up-scale the approach for extended geographical areas, further efforts are needed to establish a national level spectral library by considering standardization of sampling, analytical reference analyses and midDRIFT spectroscopy techniques.
Influence of land use on abundance, function and spatial distribution of N-cycling microorganisms in grassland soils
(2015) Keil, Daniel; Kandeler, Ellen
This thesis focuses on the influence of land use on the abundance, function and spatial distribution of N-cycling microorganisms in grassland soils, but also on soil biogeochemical properties, as well as on enzyme activities involved in the carbon-, nitrogen-, and phosphorous cycle. The objective of this thesis was tackled in three studies. All study sites that were investigated as part of this thesis were preselected and assigned according to study region and land use within the framework of the “Exploratories for Functional Biodiversity Research – The Biodiversity Exploratories” of the Deutsche Forschungsgemeinschaft priority program 1374. The first study addressed the question whether land-use intensity influences soil biogeochemical properties, as well as the abundance and spatial distributions of ammonia-oxidizing and denitrifying microorganisms in grasslands of the Schwäbische Alb. To this end, a geostatistical approach on replicated grassland sites (10 m × 10 m), belonging to either unfertilized pastures (n = 3) or fertilized mown meadows (n = 3), representing low and high land-use intensity, was applied. Results of this study revealed that land-use intensity changed spatial patterns of both soil biogeochemical properties and N-cycling microorganisms at the plot scale. For soil biogeochemical properties, spatial heterogeneity decreased with higher land-use intensity, but increased for ammonia oxidizers and nirS-type denitrifiers. This suggests that other factors, both biotic and abiotic than those measured, are driving the spatial distribution of these microorganisms at the plot scale. Furterhmore, the geostatistical analysis indicated spatial coexistence for ammonia oxidizers (amoA ammonia-oxidizing archaea and amoA ammonia-oxidizing bacteria) and nitrate reducers (napA and narG), but niche partitioning between nirK- and nirS-type denitrifiers. The second study aimed at whether land-use intensity contributes to spatial variation in microbial abundance and function in grassland ecosystems of the Schwäbische Alb assigned to either low (unfertilized pastures, n = 3), intermediate (fertilized mown pastures, n = 3), or high (fertilized mown meadows, n = 3) land-use intensity. Plot-scale (10 m × 10 m) spatial heterogeneity and autocorrelation of soil biogeochemical properties, microbial biomass and enzymes involved in C, N, and P cycle were investigated using a geostatistical approach. Geostatistics revealed spatial autocorrelations (p-Range) of chemical soil properties within the maximum sampling distance of the investigated plots, while greater variations of p-Ranges of soil microbiological properties indicated spatial heterogeneity at multiple scales. An expected decrease in small-scale spatial heterogeneity in high land-use intensity could not be confirmed for microbiological soil properties. Finding smaller spatial autocorrelations for most of the investigated properties indicated increased habitat heterogeneity at smaller scales under high land-use intensity. In the third study, the effects of warming and drought on the abundance of denitrifier marker genes, the potential denitrification activity and the N2O emission potential from grassland ecosystems located in the Schwäbische Alb, the Hainich, and the Schorfheide region were investigated. Land use was defined individually for each grassland site by a land-use index that integrated mowing, grazing and fertilization at the sites over the last three years before sampling of the soil. It was tested if the microbial community response to warming and drought depended on more static site properties (soil organic carbon, water holding capacity, pH) in interaction with land use, the study region and the climate change treatment. It was further tested to which extent the N2O emission potential was influenced by more dynamic properties, e.g. the actual water content, the availability of organic carbon and nitrate, or the size of the denitrifier community. Warming effects in enhanced the potential denitrification of denitrifying microorganisms. While differences among the study regions were mainly related to soil chemical and physical properties, the land-use index was a stronger driver for potential denitrification, and grasslands with higher land use also had greater potentials for N2O emissions. The total bacterial community did not respond to experimental treatments, displaying resilience to minor and short-term effects of climate change. In contrast, the denitrifier community tended to be influenced by the experimental treatments and particularly the nosZ abundance was influenced by drought. The results indicate that warming and drought affected the denitrifying communities and the potential denitrification, but these effects are overruled by study region and site-specific land-use index. This thesis gives novel insights into the performance of N-cycling microorganisms in grassland ecosystems. The spatial distribution of soil biogeochemical properties is strongly dependent on land-use intensity, as in return is the spatial distribution of nitrifying and denitrifying microorganisms and the ecosystem services they perform. Yet, future work will be necessary to fully understand the interrelating factors and seasonal variability, which influence the ecosystem functioning and ecosystem services that are provided by N-cycling soil microorganisms at multiple scales.
Linking microbial abundance and function to understand nitrogen cycling in grassland soils
(2017) Regan, Kathleen Marie; Kandeler, Ellen
This thesis characterized spatial and temporal relationships of the soil microbial community, the nitrogen cycling microbial community, and a subset of the nitrogen cycling community with soil abiotic properties and plant growth stages in an unfertilized temperate grassland. Unfertilized perennial grasslands depend solely on soil-available nitrogen and in these environments nitrogen cycling is considered to be both highly efficient and tightly coupled to plant growth. Unfertilized perennial grasslands with high plant diversity, such as ours, have also been shown to have higher soil organic carbon, total nitrogen, and microbial carbon; greater food web complexity; and more complex biological communities than more intensively managed grasslands or croplands. This made the choice of study plot especially well-suited for characterizing the relationships we sought to identify, and made it possible to detect spatial and temporal patterns at a scale that has heretofore been under-examined. The first study used a combination of abiotic, plant functional group, and PLFA measurements together with spatial statistics to interpret spatial and temporal changes in the microbial community over a season. We found that its overall structure was strongly related to the abiotic environment throughout the sampling period. The strength of that relationship varied, however, indicating that it was not constant over time and that other factors also influenced microbial community composition. PLFA analysis combined with principal components analysis made it possible to discern changes in abundances and spatial distributions among Gram-positive and Gram-negative bacteria as well as saprotrophic fungi. Modeled variograms and kriged maps of the changes in distributions of exemplary lipids of both bacterial groups also showed distinct differences in their distributions on the plot, especially at stages of most rapid plant growth. Although environmental properties were identified as the main structuring agents of the microbial community, components of those environmental properties varied over the season, suggesting that plant growth stage had an indirect influence, providing evidence of the complexity and dynamic nature of the microbial community in a grassland soil. The second study took the same analytical approach, this time applying it to abundances of key members of the soil nitrogen cycling community. Marker genes for total archaea and bacteria, nitrogen fixing bacteria, ammonia oxidizing archaea and bacteria, and denitrifying bacteria were quantified by qPCR. Potential nitrification activity and denitrifying enzyme activity were also determined. We found clear seasonal changes in the patterns of abundance of the measured genes and could associate these with changes in substrate availability related to plant growth stages. Most strikingly, we saw that small and ephemeral changes in soil environmental conditions resulted in changes in these microbial communities, while at the same time, process rates of their respective potential enzyme activities remained relatively stable. This suggests both short term niche-partitioning and functional redundancy within the nitrogen cycling microbial community. The seasonal changes in abundances we observed also provided additional evidence of a dynamic relationship between microorganisms and plants, an important mechanism controlling ecosystem nitrogen cycling. The third study determined spatial and temporal interactions between AOA, AOB and NOB. These steps are related in both space and time, as the ammonia-oxidizers provide the necessary substrate for nitrite-oxidizers. Using a combination of spatial statistics and phylogenetic analysis, our data indicated seasonally varying patterns of niche differentiation between the two bacterial groups, Nitrospira and Nitrobacter in April, but more homogeneous patterns by August which may have been due to different strategies for adapting to changes in substrate concentrations resulting from competition with plants. We then asked a further question: was the microbial structure at sampling sites with high NS gene abundances fundamentally different from those with low NS gene abundances? Using a phylogenetic approach, the operational taxonomic unit composition of NS was analyzed. Community composition did not change over the first half of the season, but by the second half, the relative proportion of a particular OTU had increased significantly. This suggested an intraspecific competition within the NS and the possible importance of OTU 03 in nitrite oxidation at a specific period of time. Observed positive correlations between AOA and Nitrospira further suggested that in this unfertilized grassland plot, the nitrification process may be predominantly performed by these groups, but is restricted to a limited timeframe.
Nutrient management and spatial variability of soils across scales and settlement schemes in Zimbabwe
(2010) Cobo Borrero, Juan Guillermo; Cadisch, Georg
Decline in soil fertility in Africa is one of the most limiting biophysical factors to agricultural productivity, as nutrient mining and low yields are strongly related. However, the high heterogeneity in management together with different biophysical, socio-economical and political conditions across each African agro-ecosystem make blanket recommendations difficult. Thus, acknowledging heterogeneity, and moreover quantifying it at different spatial scales, are the first steps to make adequate recommendations for the different actors. The goal of this thesis was to develop new methodological approaches to better understand nutrient management and spatial variability of soils across different scales in African agro-ecosystems, having various small-holder settlement schemes in Zimbabwe as a case study. Firstly, the thesis includes a literature review on nutrient balances in Africa, which was carried out to illustrate main approaches, challenges, and progress made, with emphasis on issues of scale. The review revealed that nutrient balances are widely used across the continent. The collected dataset from 57 peer-reviewed studies indicated, however, that most of the balances were calculated at plot and farm scale, and generated in East Africa. Data confirmed the expected trend of negative balances for N and K (>75% of studies had mean values below zero), while for P only 56% of studies showed negative mean balances. Several cases with positive nutrient balances indicated that soil nutrient mining cannot be generalized across the African continent. Land use systems of wealthier farmers and plots located close to homesteads mostly presented higher N and P balances than systems of poorer farmers (p<0.001) and plots located relatively farther away (p<0.05). Partial nutrient balances were significantly higher (p<0.001) than full balances calculated for the same systems, but the latter carried more uncertainties. The change in magnitude of nutrient balances from plot to continental level did not show any noticeable trend, which challenges prevailing assumptions that a trend exists. However, methodological differences made a proper inter-scale comparison of results difficult. Actually, the review illustrated the high diversity of methods used to calculate nutrient balances and highlighted the main pitfalls, especially when nutrient flows and balances were scaled-up. In fact, gathered information showed that despite some few initiatives, appropriate scaling-up methods are still incipient. In the next chapter, the nutrient balance approach was applied in NE Zimbabwe. Three smallholder villages located in a typical communal area (colonial settlement from 1948), and in old (1987) and new (2002) resettlement areas (post- land reform settlements), on loamy sand, sandy loam and clay soils, respectively, were selected to explore differences in natural resource management and land productivity. Focus group discussions and surveys were carried out with farmers. Additionally, farmers in three wealth classes per village were chosen for a detailed assessment of their main production systems. Maize grain yields (Mg ha-1) in the communal (1.5-4.0) and new resettlement areas (1.9-4.3) were similar but significantly higher than in the old resettlement area (0.9-2.7), despite lower soil quality in the communal area. Nutrient input use was the main factor controlling maize productivity in the three areas (R2=59-83%), while inherent soil fertility accounted for up to 12%. Partial N balances (kg ha-1 yr-1) were significantly lower in the new resettlement (-9.1 to +14.3) and old resettlement (+7.4 to +9.6) than in the communal area (+2.1 to +59.6) due to lower nutrient applications. P balances were usually negative. Consistently, maize yields, nutrient applications and partial N balances were higher for the high wealth class than in poorer classes. It is argued that effective policies supporting an efficient fertilizer distribution and improved soil management practices, with clearer rights to land, are necessary to avoid future land degradation and to improve food security in Zimbabwe, particularly in the resettlement areas. In the last chapter, the same three villages in NE Zimbabwe were sampled to determine the feasibility of integrating mid-infrared spectroscopy (MIRS) and geostatistics, as a way of facilitating landscape analysis and monitoring. A nested non-aligned design with hierarchical grids of 750, 150 and 30 m resulted in 432 sampling points across all villages. At each point, a composite topsoil sample was taken and analyzed by MIRS. Conventional laboratory analyses on 25-38% of the samples were used for the prediction of concentration values on the remaining samples through the application of MIRS - partial least squares regression models. Models were successful (R2≥0.89) for sand, clay, pH, total C and N, exchangeable Ca, Mg and effective CEC; but not for silt, available P, and exchangeable K and Al (R20.82). Minimum sample sizes required to accurately estimate the mean of each soil property in each village were calculated. With regard to locations, fewer samples were needed in the new resettlement area than in the other two areas; regarding parameters, least samples were needed for estimating pH and sand. Spatial analyses of soil properties in each village were undertaken by constructing standardized isotropic semivariograms, which were usually well described by spherical models. Spatial autocorrelation of most variables was displayed over ranges of 250-695 m. The nugget-to-sill ratios showed that overall spatial dependence of soil properties was: new resettlement > old resettlement > communal area; which was attributed to both intrinsic (e.g. texture) and extrinsic (e.g. management) factors. As a new approach, geostatistical analysis was performed directly using MIRS data, after principal component analyses, where the first three components explained 70% of the overall variability. Semivariograms based on these components showed that spatial dependence per village was similar to overall dependence identified from individual soil properties in each area. The first component (explaining 49% of variation) related well with all soil properties of reference samples (absolute correlation values of 0.55-0.96). This demonstrated that MIRS data could be directly linked to geostatistics for a broad and quick evaluation of soil spatial variability. Integrating MIRS with geostatistical analyses is a cost-effective promising approach, i.e. for soil fertility and carbon sequestration assessments, mapping and monitoring at landscape level.
Optimizing the prediction of genotypic values accounting for spatial trend andpopulation structure
(2010) Müller, Bettina Ulrike; Piepho, Hans-Peter
Different effects, like the design of the field trial, agricultural practice, competition between neighboured plots, climate as well as the spatial trend, have an influence on the non-genotypic variation of the genotype. This effects influence the prediction of the genotypic value by the non-genotypic variation. The error, which results from the influence of the non-genotypic variation, can be separated from the phenotypic value by field design and statistical models. The integration of different information, like spatial trend or marker, can lead to an improved prediction of genotypic values. The present work consists of four studies from the area of plant breeding and crop science, in which the prediction of the genotypic values was optimized with inclusion of the above mentioned aspects. Goals of the work were: (1) to compare the different spatial models and to find one model, which is applicable as routine in plant breeding analysis, (2) to optimize the analysis of unreplicated trials of plant breeding experiments by improving the allocation of replicated check genotypes, (3) to improve the analysis of intercropping experiments by using spatial models and to detect the neighbour effect between the different cultivars, and (4) to optimize the calculation of the genome-wide error rate in association mapping experiments by using an approach which regards the population structure. Different spatial models and a baseline model, which reflects the randomization of the field trial, were compared in three of the four studies. In one study the models were compared on basis of different efficiency criteria with the goal to find a model, which is applicable as routine in plant breeding experiments. In the second study the different spatial models and the baseline model were compared on unreplicated trials, which are used in the early generation of the plant breeding process. Adjacent to the comparison of the models in this study different designs were compared with the goal to see if a non-systematic allocation of check genotypes is more preferable than a systematic allocation of check genotypes. In the third study these different models were tested for intercropping experiments. In this study it should be tested, if an improvement is expectable for these non randomized or restricted randomized trials by using a spatial analysis. The results of the three studies are that no spatial model could be found, which is preferable over all other spatial models. In a lot of cases the baseline model, which regards only the randomization, but no spatial trend, was better than the spatial models, also for the restricted or non-randomized intercropping trials. In all three studies the basic principle was followed to start first with the baseline model, which is based on the randomization theory, and then to extend it by spatial trend, if the model fit can be improved. In the second study the systematic and non-systematic allocation of check plots in unreplicated trials were compared to solve the question if a non-systematic allocation leads to more efficient estimates of genotypes as the systematic allocation. The non-systematic allocation of check plots led to an unbiased estimation in three of four uniformity trials. As well as in the third study an analysis was done, if the border plots of the different cultivars are influenced by the neighboured cultivar and if there are significant differences to the inner plot. The position of the cultivars, border plot or inner plot, had a significant influence of the yield. If maize was cultivated adjacent to pea, the yield of the border plot of maize was much higher than the inner plot of maize. When wheat was cultivated behind maize, there were no significant differences in the yield, if the plot was a border plot or inner plot. In addition to optimizing the field design for unreplicated trials and the extension of the models by spatial trend the marker information was integrated in a fourth study. An approach was proposed in this study, which calculates the genome wide error for association mapping experiments and accounts for the population structure. Advantages of this approach in contrast to previously published approaches are that the approach on the one hand is not too conservative and on the other hand accounts the population structure. The adherence of the genome wide error rate was tested on three datasets, which were provided by different plant breeding companies. The results of these studies, which were obtained in this thesis, show that by the different extensions, like integration of spatial trend and marker information, and modifications of the field design, an improved prediction of the genotypic values can be achieved.