Soil organic carbon dynamics in pastures established after deforestation in the humid tropics of Costa Rica

Currently, rates of deforestation in the tropics are probably higher than ever before in the past. As a consequence, changes in the earth's physical and chemical environments are proceeding at unprecedented rates. Increasing atmospheric concentrations of CO 2 , N 2 O and other trace gases, caused by enhanced emissions from soils after forest clearing, show that deforestation in tropical areas is of global importance. Recent estimates suggest a net release of carbon from the world's tropics, due to deforestation, of between 0.42 and 1.60 Pg C yr -1(1 Pg = 10 15g) of which 0.1 to 0.3 Pg C yr -1are attributed to decreases in soil organic matter content. This carbon release from tropical areas is second only to the global release from the burning of fossil fuels (which is about 5.3 Pg C yr -1).The main objective of this thesis was to quantify the changes in soil organic carbon storage and the resulting release of CO 2 after the conversion of tropical rain forest to pasture on two contrasting soil types in the humid tropics of Costa Rica. To study changes in soil organic carbon storage, sites of an Andisol and an Inceptisol, cleared at different times in the past (deforestation sequences) were compared. A deforestation map, based on aerial photographs from the period 1952 - 1984, was made for a part of the Atlantic Zone of Costa Rica, providing a well documented history of forest clearing. Using GIS techniques, this deforestation map was combined with an available soil map to select the study sites. Analysis of deforestation patterns on the map demonstrated a close relation of deforestation rate with accessibility and soil quality.Soil organic matter levels are the result of complex production and decomposition processes. The input of carbon from grass plant roots into the soil was quantified, using pulse labelling with 14C. The pulse labelling experiment revealed that root dry matter production of an improved pasture like Brachiaria (12 Mg ha -1yr -1) was about twice the root production of a low-productive species like Axonopus (6 Mg ha-1 yr-1). Root biomass of Brachiaria was about three times the root biomass of Axonopus due to higher residence time of carbon in the root biomass of Brachiaria as compared to Axonopus . Root exudates of grass plants were found to have a minor direct contribution to the longer term carbon dynamics, either because exudation rate was small or because decomposition was fast and complete.Decomposition of soil organic matter was measured using the δ 13C method, which uses differences in natural 13C isotope levels in vegetation (C3 and C4 vegetation) and soil organic matter to calculate changes in soil organic carbon. The method is applicable in soil organic matter studies where a change from C3 to C4 vegetation has occurred (or vice versa). It was demonstrated that for a correct application of the method, detailed information of changes in bulk densities accompanying changes in land use was vital. An uncertainty analysis of the δ 13C method demonstrated that the output of the δ 13C method in soil organic matter studies was highly variable due to variations in the input data. Spatial variability was the main source of the uncertainty in input data. However, variations due to sampling error and short scale variability were considerable and should not be ignored.Information on carbon input and decomposition was integrated, using a simple structured soil organic carbon (SOC) model which included carbon isotope fractionation during decomposition and depth dependent decomposition and humification rates. With this model, the observed changes in soil organic carbon and corresponding δ 13C levels during the conversion from a humid tropical forest to a cattle pasture were simulated successfully for the two soil types. With the calibrated model the cumulative net C02 release was calculated. The cumulative net release of CO 2 for pastures with low productive grass species (Axonopus compressus), varied from 31.5 (Humitropept) to 60.5 Mg C ha -1(Hapludand) in the first 20 years after forest clearing. These cumulative emissions could be reduced to 12.0 and 24.7 Mg C ha -1respectively, if higher productive grass species (e.g. Brachiaria dictyoneura ) would be introduced into the area.Decomposition rates were strongly influenced by depth. Inclusion of deeper layers in soil organic carbon simulation studies and considering carbon isotopes will probably improve the performance of SOC models in long-term studies

Transforum system innovation towards sustainable food. A review

Innovations in the agri-food sector are needed to create a sustainable food supply. Sustainable food supply requires unexpectedly that densely populated regions remain food producers. A Dutch innovation program has aimed at showing the way forward through creating a number of practice and scientific projects. Generic lessons from the scientific projects in this program are likely to be of interest to agricultural innovation in other densely populated regions in the world. Based on the executed scientific projects, generic lessons across the whole innovation program are derived. We found that the agricultural sector requires evolutionary rather than revolutionary changes to reshaping institutions. Measuring sustainability is possible against benchmarks and requires stakeholder agreement on sustainability values. Results show the importance of multiple social views and multiple stakeholder involvement in agricultural innovation. Findings call for flexible goal rather than process-oriented management of innovation. Findings also emphasise the essential role of profit in anchoring sustainable development in business. The results agree with concepts of evolutionary innovation. We conclude that there is no single best solution to making the agri-food sector more sustainable densely populated areas, but that the combination of a range of solutions and approaches is likely to provide the best way forward

Solving ARC visual analogies with neural embeddings and vector arithmetic: A generalized method

Analogical reasoning derives information from known relations and generalizes this information to similar yet unfamiliar situations. One of the first generalized ways in which deep learning models were able to solve verbal analogies was through vector arithmetic of word embeddings, essentially relating words that were mapped to a vector space (e.g., king - man + woman = __?). In comparison, most attempts to solve visual analogies are still predominantly task-specific and less generalizable. This project focuses on visual analogical reasoning and applies the initial generalized mechanism used to solve verbal analogies to the visual realm. Taking the Abstraction and Reasoning Corpus (ARC) as an example to investigate visual analogy solving, we use a variational autoencoder (VAE) to transform ARC items into low-dimensional latent vectors, analogous to the word embeddings used in the verbal approaches. Through simple vector arithmetic, underlying rules of ARC items are discovered and used to solve them. Results indicate that the approach works well on simple items with fewer dimensions (i.e., few colors used, uniform shapes), similar input-to-output examples, and high reconstruction accuracy on the VAE. Predictions on more complex items showed stronger deviations from expected outputs, although, predictions still often approximated parts of the item's rule set. Error patterns indicated that the model works as intended. On the official ARC paradigm, the model achieved a score of 2% (cf. current world record is 21%) and on ConceptARC it scored 8.8%. Although the methodology proposed involves basic dimensionality reduction techniques and standard vector arithmetic, this approach demonstrates promising outcomes on ARC and can easily be generalized to other abstract visual reasoning tasks.Comment: Data and code can be found on https://github.com/foger3/ARC_DeepLearnin

Nitrous oxide fluxes and nitrogen cycling along a pasture chronosequence in Central Amazonia, Brazil

International audienceWe studied nitrous oxide (N2O) fluxes and soil nitrogen (N) cycling following forest conversion to pasture in the central Amazon near Santarém, Pará, Brazil. Two undisturbed forest sites and 27 pasture sites of 0.5 to 60 years were sampled once each during wet and dry seasons. In addition to soil-atmosphere fluxes of N2O we measured 27 soil chemical, soil microbiological and soil physical variables. Soil N2O fluxes were higher in the wet season than in the dry season. Fluxes of N2O from forest soils always exceeded fluxes from pasture soils and showed no consistent trend with pasture age. At our forest sites, nitrate was the dominant form of inorganic N both during wet and dry season. At our pasture sites nitrate generally dominated the inorganic N pools during the wet season and ammonium dominated during the dry season. Net mineralization and nitrification rates displayed large variations. During the dry season net immobilization of N was observed in some pastures. Compared to forest sites, young pasture sites (?2 years) had low microbial biomass N and protease activities. Protease activity and microbial biomass N peaked in pastures of intermediate age (4 to 8 years) followed by consistently lower values in older pasture (10 to 60 years). The C/N ratio of litter was low at the forest sites (~25) and rapidly increased with pasture age reaching values of 60-70 at pastures of 15 years and older. Nitrous oxide emissions at our sites were controlled by C and N availability and soil aeration. Fluxes of N2O were negatively correlated to leaf litter C/N ratio, NH4+-N and the ratio of NO3--N to the sum of NO3--N + NH4+-N (indicators of N availability), and methane fluxes and bulk density (indicators of soil aeration status) during the wet season. During the dry season fluxes of N2O were positively correlated to microbial biomass N, ?-glucosidase activity, total inorganic N stocks and NH4+-N. In our study region, pastures of all age emitted less N2O than old-growth forests, because of a progressive decline in N availability with pasture age combined with strongly anaerobic conditions in some pastures during the wet season

The effect of climate type on timescales of drought propagation in an ensemble of global hydrological models

Drought is a natural hazard that occurs at many temporal and spatial scales and has severe environmental and socioeconomic impacts across the globe. The impacts of drought change as drought evolves from precipitation deficits to deficits in soil moisture or streamflow. Here, we quantified the time taken for drought to propagate from meteorological drought to soil moisture drought and from meteorological drought to hydrological drought. We did this by cross-correlating the Standardized Precipitation Index (SPI) against standardized indices (SIs) of soil moisture, runoff, and streamflow from an ensemble of global hydrological models (GHMs) forced by a consistent meteorological dataset. Drought propagation is strongly related to climate types, occurring at sub-seasonal timescales in tropical climates and at up to multi-annual timescales in continental and arid climates. Winter droughts are usually related to longer SPI accumulation periods than summer droughts, especially in continental and tropical savanna climates. The difference between the seasons is likely due to winter snow cover in the former and distinct wet and dry seasons in the latter. Model structure appears to play an important role in model variability, as drought propagation to soil moisture drought is slower in land surface models (LSMs) than in global hydrological models, but propagation to hydrological drought is faster in land surface models than in global hydrological models. The propagation time from SPI to hydrological drought in the models was evaluated against observed data at 127 in situ streamflow stations. On average, errors between observed and modeled drought propagation timescales are small and the model ensemble mean is preferred over the use of a single model. Nevertheless, there is ample opportunity for improvement as substantial differences in drought propagation are found at 10&thinsp;% of the study sites. A better understanding and representation of drought propagation in models may help improve seasonal drought forecasting as well as constrain drought variability under future climate scenarios.</p

An Algorithm for constructing Hjelmslev planes

Projective Hjelmslev planes and Affine Hjelmselv planes are generalisations of projective planes and affine planes. We present an algorithm for constructing a projective Hjelmslev planes and affine Hjelsmelv planes using projective planes, affine planes and orthogonal arrays. We show that all 2-uniform projective Hjelmslev planes, and all 2-uniform affine Hjelsmelv planes can be constructed in this way. As a corollary it is shown that all 2-uniform Affine Hjelmselv planes are sub-geometries of 2-uniform projective Hjelmselv planes.Comment: 15 pages. Algebraic Design Theory and Hadamard matrices, 2014, Springer Proceedings in Mathematics & Statistics 13