Nitrogenous and Phosphorus Soil Contents in Tierra del Fuego Forests: Relationships with Soil Organic Carbon, Climate, Vegetation and Landscape Metrics

Soil nitrogen (SN) and soil phosphorus (SP) contents support several ecosystem services and define the forest type distribution at local scale in Southern Patagonia. The quantification of nutrients during forest surveys requires soil samplings and estimations that are costly and difficult to measure. For this, predictive models of soil nutrients are needed. The objective of this study was to quantify SN and SP contents (30 cm depth) using different modelling approaches based on climatic, topographic and vegetation variables. We used data from 728 stands of different forest types for linear regression models to map SN and SP. The fitted models captured the variability of forest types well (R²-adj. 92–98% for SN and 70–87% for SP). The means were 9.3 ton ha−1 for SN and 124.3 kg ha−1 for SP. Overall, SN values were higher in the deciduous forests than those in the mixed evergreen, while SP was the highest in the Nothofagus pumilio forests. SN and SP are relevant metrics for many applications, connecting major issues, such as forest management and conservation. With these models, the quantification of SN and SP stocks across forests of different protection status (National Law 26,331/07) and national/provincial reserve networks is possible, contributing to the determination of nutrient contents at landscape level.Fil: Martínez Pastur, Guillermo José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas; ArgentinaFil: Aravena Acuña, Marie Claire Alejandra. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas; ArgentinaFil: Chaves, Jimena Elizabeth. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas; ArgentinaFil: Cellini, Juan Manuel. Universidad Nacional de La Plata. Facultad de Ciencias Agrarias y Forestales. Laboratorio de Investigaciones en Maderas; ArgentinaFil: Silveira, Eduarda M. O.. University of Florida. Department of Wildlife Ecology and Conservation; Estados UnidosFil: Rodriguez Souilla, Julian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas; ArgentinaFil: Von Müller, Axel Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Sur. Estación Experimental Agropecuaria Esquel; ArgentinaFil: la Manna, Ludmila Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de la Patagonia "San Juan Bosco". Facultad de Ingeniería - Sede Esquel. Centro de Estudios Ambientales Integrados; ArgentinaFil: Lencinas, María Vanessa. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas; ArgentinaFil: Peri, Pablo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria; Argentina. Universidad Nacional de la Patagonia Austral; Argentin

Variabilita zásob uhlíku v půdě a možnost využití GPR radaru k jejich zjišťování

In the context of ongoing climate change, more attention is being given to soil and its organic carbon pool. This is because soil could partially compensate for the increasing amount of carbon dioxide in the atmosphere or, on the other hand, be a vast pool of carbon dioxide if organic matter stored in soil mineralizes. Therefore, the precision of soil organic carbon pool estimation, development of monitoring methods, and revelation of factors controlling the pool have been more and more focused on by soil scientists. Conventional soil sampling for soil organic carbon pool estimation and modelling includes manual sampling, measuring forest floor depth and bulk density, and taking soil samples for carbon concentration analysis. These are time and labour demanding. Therefore, there is an effort to develop precise models predicting the carbon pool based on its driving factors that would limit the amount of fieldwork. The models often use remote sensing data, and, in addition, there is an effort to estimate soil organic carbon concentration from soil spectral characteristics. Nevertheless, another variable needed to estimate the organic carbon pool is the thickness of the soil profile or individual soil horizons. The thickness can hardly be determined from remote sensing data, so it has to be measured...V souvislosti s probíhající klimatickou změnou zapříčiněnou zejména růstem oxidu uhličitého v atmosféře, je stále více pozornosti věnováno výpočtu organického uhlíku v půdě a možnostem jeho sekvestrace. Půda je největším terestrickým zásobníkem uhlíku a může zpomalovat stoupající množství oxidu uhličitého v atmosféře jeho sekvestrací nebo v opačném případě být významným zdroje oxidu uhličitého, pokud by došlo k mineralizaci organického uhlíku uloženého v půdě. Proto se pedologie stale více zabývá zpřesňováním odhadů uhlíkových zásob, vývojem metod jejich monitorování a hledáním faktorů, které sekvestraci a stabilizaci uhlíku v půdě ovlivňují. Konvenční sběr dat za účelem odhadů zásob uhlíku v půdě sestává z manuálního terénního průzkumu pomocí půdních sond, měření mocností horizontů a odběru vzorků pro stanovení obsahu organického uhlíku. Tyto práce jsou však časově i finančně značně náročné. Proto je snahou nalézt faktory, které zásobu organického uhlíku ovlivňují a na jejich základě predikovat množství uhlíku v místech, kde půdní průzkum nebyl proveden. Významný posun přinesl i dálkový průzkum země, který umožňuje odhadovat koncentraci půdního organického uhlíku na základě spektrální odrazivosti půdy. Nicméně, jedním z klíčových parametrů potřebných pro odhad zásob uhlíku v půdě je mocnost...Department of Physical Geography and GeoecologyKatedra fyzické geografie a geoekologiePřírodovědecká fakultaFaculty of Scienc

Fire Effects on Soil Organic Matter in the Creek Fire

Wildfires have increased in frequency and severity over the past few decades due to the increased concertation of CO2 emissions from anthropogenic influence. Soil carbon (C) sequestration has been identified as a climate change mitigation strategy; however, the influx of large-scale wildfires has accelerated landscape processes such as erosion, reducing soil aggradation, and soil C and nitrogen (N) protection. This trend is highlighted by the Creek Fire that occurred in September 2020 and burned 379,895 acres in the Sierra National Forest. This research is designed to close the knowledge gap regarding the impact of burn severity on soil organic matter (SOM) C and N distribution at the landscape scale in California. To accomplish this, 70 soil samples were collected two years following the Creek Fire at a depth of 0-5 cm. These SOM samples were separated into mineral-associated organic matter (MAOM) and particulate organic matter (POM) to better understand how these size fractions influence soil C and N stocks. Alone burn severity is not statistically significant; however, the multiple variable regression analysis shows that Landsat dNBR burn severity is a significant predictor variable for all MAOM-C, MAOM-N, POM-C, and POM-N when coupled with other predictor variables. Additional predictor variables with significance include lithology variables, vegetation variables such as moisture and total greenness, and topography variables such as elevation and roughness. The POM-N model was the best at depicting SOM relationships, by explaining 48% of the variance in POM-N; therefore, a predictive map was created to depict this relationship. The results of this study provide valuable information and context regarding post-fire SOM C and N storage and can be used to inform future management decisions involving landscape restoration

Predicting Soil Organic Carbon and Nitrogen Content Using Airborne Laser Scanning in the Taita Hills, Kenya

Reducing greenhouse gas emissions and increasing carbon sequestration is critical for climate change mitigation. With the emergence of carbon markets and the development of compensatory mechanisms such as Reducing Emissions from Deforestation and Degradation in Developing Countries (REDD+), there is much interest in measurement and monitoring of soil organic carbon (SOC). Detailed information on the distribution of SOC and other soil attributes, such as nitrogen (N), across the landscape is necessary in order to locate areas where carbon stocks can be increased and loss of soil carbon slowed down. SOC has large spatial variability, which often demands intensive sampling in the field. Airborne laser scanning (ALS) provides very accurate information about the topography and vegetation of the measured area, and hence, possible means for improving soil properties maps. In this thesis, the aim was to study the feasibility of ALS and free of cost ancillary data for predicting SOC and N in a tropical study area. The study area is located in the Taita Hills, in South-Eastern Kenya, and has highly fluctuating topography ranging between 930–2187 m. Land cover in the Taita Hills is very heterogeneous and consists of forest, woodlands, agroforestry and croplands. The field data consisted of SOC and N measurements for 150 sample plots (0.1 ha). The soil samples along with several other soil and vegetation attributes were collected in 2013. ALS (Optech ALTM 3100, mean return density 11.4 m-1) data was acquired in February 2013. ALS data was pre-processed by classifying ground, low- and high vegetation, buildings and power wires. ALS point cloud was used to calculate two types of predictors for SOC and N: 1) topographical variables based on the high resolution digital terrain model (DTM) and 2) ALS metrics describing the vertical distribution and cover of vegetation. The ancillary datasets included spectral predictors based on Landsat 7 ETM+ time series and soil grids for Africa at 250 m resolution. In total, over 500 potential predictors were calculated for the modelling. Random Forest model was constructed from the selected variables and model performance was analysed by comparing the predicted values to the field measurements. The best model for SOC had pseudo R2 of 0.66 and relative root mean square error (RRMSE) of 30.98 %. Best model for N had pseudo R2 of 0.43 and RRMSE of 32.14 %. Usage of Landsat time series as ancillary dataset improved the modelling results slightly. For SOC, the most important variables were tangential curvature, maximum intensity and Landsat band 2 (green). Finally, the best model was applied for mapping SOC and N in the study area. The results of this study are in line with other remote sensing studies modelling soil properties in Africa. The soil properties in the study area do not correlate strongly with present vegetation and topography leading to intermediate modelling results.Kasvihuonepäästöjen vähentäminen ilmakehästä on kriittistä ilmastonmuutoksen hillitsemisen kannalta. Hiilimarkkinoiden ja erilaisten korvausmekanismien kehittyminen on lisännyt kiinnostusta maaperässä olevan orgaanisen hiilen mittaamiseen ja monitoroimiseen. Yksityiskohtainen tieto maaperän ominaisuuksista, kuten orgaanisen hiilen ja typen alueellisesta jakaumasta, voi auttaa löytämään alueita, joissa hiilen osuutta voidaan kasvattaa tai sen vähentymistä voidaan hidastaa. Maaperän hiilen vaihtelevasta spatiaalisesta jakaumasta johtuen kalliita kenttämittauksia tarvitaan runsaasti. Lentolaserkeilaus tarjoaa tarkkaa tietoa kuvatun alueen topografiasta ja kasvillisuudesta, mikä voisi olla hyödyllistä maaperän karttojen laadun parantamisessa. Tämän tutkimuksen tavoitteena oli selvittää, miten lentolaserkeilaus ja vapaasti saatavilla oleva lisäaineisto soveltuvat maaperän orgaanisen hiilen ja typen pitoisuuksien ennustamiseen. Tutkimusalue on Taitavuorilla Kaakkois-Keniassa, jossa topografia on hyvin vaihtelevaa, korkeuden vaihdellessa 930 ja 2187 metrin välillä. Taitavuorten maanpeite on hyvin heterogeenistä ja koostuu metsistä, metsämaasta, peltometsäviljelysmaista ja viljelysmaista. Tutkimuksessa käytetty kenttäaineisto koostuu 150:sta maaperän hiili- ja typpimittauksista 0.1 hehtaarin kokoisilta koealoilta. Maaperän mittaukset suoritettiin vuonna 2013. Lentolaserkeilausaineisto (Optech ALTM 3100) kuvattiin helmikuussa 2013. Kuvattu lentolaserkeilausaineisto esikäsiteltiin luokittelemalla maaperä, matala ja korkea kasvillisuus, rakennukset ja voimalinjat. Lentolaserkeilausaineistoa käytettiin kahden tyyppisten muuttujien laskennassa: 1) topografiamuuttujat, jotka laskettiin erittäin korkearesoluutioisesta korkeusmallista ja 2) kasvillisuuden vertikaalisesta rakenteesta ja peitosta kertoviin muuttujiin. Lisäaineistona analyysissä oli mukana spektraalista tietoa sisältävä Landsat ETM+ aikasarja, sekä maaperäruudukot Afrikasta 250 m:n spatiaalisella resoluutiolla. Yhteensä noin 500 muuttujaa laskettiin mallinnusta varten. Random Forest -malli rakennettiin valituista muuttujista ja mallien suorituskykyä arvioitiin vertaamalla ennustettuja arvoja havaittuihin arvoihin. Parhaan maaperän hiilimallin valeselitysaste oli 0.66 ja suhteellinen keskivirhe 30.98 %. Parhaan typpimallin valeselitysaste oli 0.43 ja suhteellinen keskivirhe 32.14 %. Tärkeimmät muuttujat maaperän hiilen ennustamiseen olivat tangentiaalinen kaarevuus (tangential curvature), maksimi-intensiteetti (maximum intensity) ja Landsatin kanava 2 (vihreä aallonpituus). Landsat aineiston käyttö avustavana aineistona johti pieniin parannuksiin mallinnuksessa. Lopulta maaperän hiili- ja typpikartat ennustettiin käyttämällä parhaita löydettyjä malleja. Tämän tutkimuksen tulokset ovat linjassa muiden kaukokartoitusta hyödyntävien maaperän ominaisuuksia tutkivien tutkimuksien kanssa. Maaperän ominaisuudet eivät korreloineet voimakkaasti kasvillisuuden ja topografian kanssa, mikä johti keskinkertaisiin tuloksiin

Dissolved organic carbon in tropical watersheds : Linking field observation and eco-hydrological modelling

Dissolved organic carbon (DOC) is a general description of the organic material dissolved in water. DOC is an important source of energy, carbon, and nutrient transfers from terrestrial to aquatic ecosystems. The export of DOC into aquatic ecosystems may contribute to the carbon balance of terrestrial ecosystems and to water degradation. Ongoing climate and land cover changes will affect both DOC generation and transport, with implications for both terrestrial and aquatic ecosystems. An assessment of land use land cover and climate variability’s impacts on DOC export is needed for better management of ecosystems. Watersheds are fundamental units of ecosystem functioning and are therefore an interesting organizational unit when used to understand the combined effects of land use land cover and climate variability on DOC export. Some studies have been conducted to explore this impact of land cover and climate variability on DOC, but most were conducted in a temperate environment and few in a tropical environment. In this regard, this dissertation focused on the impact of land use land cover and climate variability on DOC mobilization and export in the Rukarara River Watershed (RRW), Rwanda. The main aim is to determine how different carbon input and output processes interact under climate and land cover variability to impact DOC emanating from tropical watersheds. Data used for this study include land cover maps produced from satellite imagery, daily air temperature and precipitation, digital elevation models (DEMs), water stage, flow, net primary productivity (NPP), soil properties such as total organic carbon (TOC), total nitrogen (TN), cation exchange capacity (CEC), aluminum (Al), iron (Fe), and soil texture within the RRW. Field observations were used to quantify riverine DOC loads, soil water extractable organic carbon (WEOC), DOC in percolation water (pDOC) and leached DOC (LDOC) and to describe their spatial variation and relationships with the aforementioned factors. Statistical models (including simple and quadratic regressions, general linear model, linear mixed effect models) were used to predict DOC within the study area. An eco-hydrological model, the Regional Hydro-Ecological Simulation System (RHESSys), was used to simulate streamflow and link it with stream DOC within the study area. The results of this study show that land use land cover and climate change interact to produce soil WEOC, from which a significant fraction is transported into streams, mainly through overland flow and loaded by the Rukarara River. The riverine DOC loss was low compared to the NPP of the RRW, but may affect the function of both land and water resources with the study area. The RHESSys model detected the response of the watershed to climate variability within the RRW and captured the significant monthly variability in streamflow within the RRW. This result indicates the potential use of RHESSys to estimate streamflow in the RRW and similar tropical watersheds. Stream DOC concentration was explained by simulated streamflow in the natural forest, indicating the potential use of RHESSys model simulated streamflow to predict stream DOC in the study watershed and similar ecosystems. Further studies should evaluate the performance of the RHESSys model to simulate other hydroecological processes in the tropical environment

MAPPING SOIL ORGANIC CARBON DYNAMICS OVER THE LAST DECADES IN MEDITERRANEAN AGRO-ECOSYSTEMS WITH LEGACY DATA

Summary Soil organic carbon (SOC) represents the biggest carbon pool of the biosphere, bigger than the living plant pool. In agriculture, SOC is of pivotal importance for sustainable soil management and is a main soil fertility indicator. As soils are responsible for food production and the provision of various ecosystem services, there is a sturdy interest in understanding how land use and management affect natural plant and crop growth, and ecosystem resilience and functioning. These processes require time and soil sustainability is to be evaluated in a long-term economic perspective by policy makers with the aim of maintaining adequate, and likely improved, conditions of the soil and the whole farm for the future. Thus, long-term actions for crop sustainability could also admit little short-time yield reduction if yield potential, stability and environmental health are maintained at the long-time. Food production and ecosystem services provision depend on the maintenance, or increase, of SOC in agricultural soil, since SOC act as a short-term nutrient reservoir, increase water holding capacity and soil infiltration rate, reduce soil compaction, and favour soil resilience against pollutants. These effects should be taken into account at both a narrow and broad geographical breadth. When aiming to manage SOC at broad geographical extent, a detailed knowledge of SOC distribution and likely change in time is required. However, such a knowledge relies on correct sampling method and modelling procedures that in turn depend on the environmental variability of the area under study. Mediterranean areas are frequently variable as an harbour, the area has been subjected to a high share of soil and above-ground biodiversity and experienced long cultivation history and intensification since the last century, which increased their fragility. In this environment, the acquisition of reliable information on SOC can require a highly dense sampling, which can also negatively affect some relict environment. In addition, sampling can imply a high cost for field work and laboratory analyses. The aim of my Ph.D. work was thus to investigate the main factors related to SOC spatial distribution in agricultural land under various pedoclimatic conditions in semiarid Mediterranean areas, using a legacy soil database (1968-2008) of SOC and soil bulk density. The dissertation is structured in six chapters: the first one is a general introduction where the rationale of the dissertation is explained, and the research questions are stated. The second chapter is a novel approach to systematically collecting literature from international peer-review issues, namely systematic map. The third one is an analysis of the legacy soil database, which intends to make the database ready to be used for the SOC assessment and for the digital soil mapping. The fourth chapter touches an issue dealing with SOC stock mapping with the boosted regression tree and a set of covariates to produce local SOC benchmarks to be compared with European and Global SOC maps. The fifth chapter fits in the same modelling frame and it is addressed at the SOC dynamics using the most widespread legacy sampling campaign. A high number of available spatial data were collected and computed and used to calibrate the SOC models. At this stage, due to the ungridded structure of the data, a machine learning based model has been used (Boosted Regression Trees). The last chapter is a comparison of models (geostatistical, machine learning and linear), and shows useful information about the way that the error is reported by each algorithm. Soil maps are not just produced for the sake of creating attractive geographical visualizations: they have a very precise task to fulfil, i.e. provide accurate and reliable information on soil properties that decision makers can use to plan interventions of any kind. The use of the Regression Kriging and Boosted Regression Trees models, which resulted in the best prediction performance in terms of R2 and RMSE, highlighted the SOC dependence on environmental factors, and the prediction of the agricultural land covers. All land cover groups were studied in the preliminary stage of this study (chapter 2), while only the cropland identified with the legacy data was the candidate for the development of the final models which lead to the detection of a positive SOC trend. The last chapter aimed at the comparison between geostatistical, machine learning and linear models to predict SOC in agricultural lands, and an improvement in local uncertainty estimation. The outstanding result was that SOC at the monitoring sites were accurately simulated, being in full agreement with observed data. Once more, actual data will be available and the model will be calibrated and validated, a model of SOC potential sequestration regional scale can be produced. The results of this dissertation has led to a clear and shared vision in the community regarding the selection of the estimation methods for SOC prediction needs to be based on careful considerations. It is good practice to test algorithms already used in literature for similar purposes, but it may be counterproductive to only look at an algorithm because it is new and never used before in a particular field. This sometimes happens in science where methods are selected only because fashionable and not based on real and tested experiments. In the dissertation the origin of the data was sometimes know and sometimes it has been data driven based. In particular, sampling design was based on geostatistics only in the 2008 campaign and it may well be that looking at very advanced methods like deep-learning could be interesting, but still less accurate than the geostatistical kriging based algorithms, which can also provide robust and well tested uncertainty estimations. In summary, even though we have now access to advanced algorithms it does not mean that we need to use them blindly without fully considering what we are trying to achieve with our working hypothesis and research question

Spatial Prediction of Soil Organic Carbon Stock in the Moroccan High Atlas Using Machine Learning

Soil organic carbon (SOC) is an essential component, which soil quality depends on. Thus, understanding the spatial distribution and controlling factors of SOC is paramount to achieving sustainable soil management. In this study, SOC prediction for the Ourika watershed in Morocco was done using four machine learning (ML) algorithms: Cubist, random forest (RF), support vector machine (SVM), and gradient boosting machine (GBM). A total of 420 soil samples were collected at three different depths (0-10 cm, 10-20 cm, and 20-30 cm) from which SOC concentration and bulk density (BD) were measured, and consequently SOC stock (SOCS) was determined. Modeling data included 88 variables incorporating environmental covariates, including soil properties, climate, topography, and remote sensing variables used as predictors. The results showed that RF (R-2 = 0.79, RMSE = 1.2%) and Cubist (R-2 = 0.77, RMSE = 1.2%) were the most accurate models for predicting SOC, while none of the models were satisfactory in predicting BD across the watershed. As with SOC, Cubist (R-2 = 0.86, RMSE = 11.62 t/ha) and RF (R-2 = 0.79, RMSE = 13.26 t/ha) exhibited the highest predictive power for SOCS. Land use/land cover (LU/LC) was the most critical factor in predicting SOC and SOCS, followed by soil properties and bioclimatic variables. Both combinations of bioclimatic-topographic variables and soil properties-remote sensing variables were shown to improve prediction performance. Our findings show that ML algorithms can be a viable tool for spatial modeling of SOC in mountainous Mediterranean regions, such as the study area

Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran

Estimation of the soil organic carbon content is of utmost importance in understanding the chemical, physical, and biological functions of the soil. This study proposes machine learning algorithms of support vector machines, artificial neural networks, regression tree, random forest, extreme gradient boosting, and conventional deep neural network for advancing prediction models of SOC. Models are trained with 1879 composite surface soil samples, and 105 auxiliary data as predictors. The genetic algorithm is used as a feature selection approach to identify effective variables. The results indicate that precipitation is the most important predictor driving 15 percent of SOC spatial variability followed by the normalized difference vegetation index, day temperature index of moderate resolution imaging spectroradiometer, multiresolution valley bottom flatness and land use, respectively. Based on 10 fold cross validation, the DNN model reported as a superior algorithm with the lowest prediction error and uncertainty. In terms of accuracy, DNN yielded a mean absolute error of 59 percent, a root mean squared error of 75 percent, a coefficient of determination of 0.65, and Lins concordance correlation coefficient of 0.83. The SOC content was the highest in udic soil moisture regime class with mean values of 4 percent, followed by the aquic and xeric classes, respectively. Soils in dense forestlands had the highest SOC contents, whereas soils of younger geological age and alluvial fans had lower SOC. The proposed DNN is a promising algorithm for handling large numbers of auxiliary data at a province scale, and due to its flexible structure and the ability to extract more information from the auxiliary data surrounding the sampled observations, it had high accuracy for the prediction of the SOC baseline map and minimal uncertainty.Comment: 30pages, 9 figure