Yield estimation and sowing date optimization based on seasonal climate information in the three CLARIS sites

The present article is a contribution to the CLARIS WorkPackage “Climate and Agriculture”, and aims at testing whether it is possible to predict yields and optimal sowing dates using seasonal climate information at three sites (Pergamino, Marcos Juarez and Anguil) which are representative of different climate and soil conditions in Argentina. Considering that we focus on the use of climate information only, and that official long time yield series are not always reliable and often influenced by both climate and technology changes, we decided to build a dataset with yields simulated by the DSSAT (Decision Support System for Agrotechnology Transfer) crop model, already calibrated in the selected three sites and for the two crops of interest (maize and soybean). We simulated yields for three different sowing dates for each crop in each of the three sites. Also considering that seasonal forecasts have a higher skill when using the 3-month average precipitation and temperature forecasts, and that regional climate change scenarios present less uncertainty at similar temporal scales, we decided to focus our analysis on the use of quarterly precipitation and temperature averages, measured at the three sites during the crop cycle. This type of information is used as input (predictand) for non-linear statistical methods (Multivariate Adaptive Regression Splines, MARS; and classification trees) in order to predict yields and their dependency to the chosen sowing date. MARS models show that the most valuable information to predict yield amplitude is the 3-month average precipitation around flowering. Classification trees are used to estimate whether climate information can be used to infer an optimal sowing date in order to optimize yields. In order to simplify the problem, we set a default sowing date (the most representative for the crop and the site) and compare the yield amplitudes between such a default date and possible alternative dates sometimes used by farmers. Above normal average temperatures at the beginning and the end of the crop cycle lead to respectively later and earlier optimal sowing. Using this classification, yields can be potentially improved by changing sowing date for maize but it is more limited for soybean. More generally, the sites and crops which have more variable yields are also the ones for which the proposed methodology is the most efficient. However, a full evaluation of the accuracy of seasonal forecasts should be the next step before confirming the reliability of this methodology under real conditions.     

Projection of future climate change conditions using IPCC simulations, neural networks and Bayesian statistics. Part 2: Precipitation mean state and seasonal cycle in South America

Evaluating the response of climate to greenhouse gas forcing is a major objective of the climate community, and the use of large ensemble of simulations is considered as a significant step toward that goal. The present paper thus discusses a new methodology based on neural network to mix ensemble of climate model simulations. Our analysis consists of one simulation of seven Atmosphere–Ocean Global Climate Models, which participated in the IPCC Project and provided at least one simulation for the twentieth century (20c3m) and one simulation for each of three SRES scenarios: A2, A1B and B1. Our statistical method based on neural networks and Bayesian statistics computes a transfer function between models and observations. Such a transfer function was then used to project future conditions and to derive what we would call the optimal ensemble combination for twenty-first century climate change projections. Our approach is therefore based on one statement and one hypothesis. The statement is that an optimal ensemble projection should be built by giving larger weights to models, which have more skill in representing present climate conditions. The hypothesis is that our method based on neural network is actually weighting the models that way. While the statement is actually an open question, which answer may vary according to the region or climate signal under study, our results demonstrate that the neural network approach indeed allows to weighting models according to their skills. As such, our method is an improvement of existing Bayesian methods developed to mix ensembles of simulations. However, the general low skill of climate models in simulating precipitation mean climatology implies that the final projection maps (whatever the method used to compute them) may significantly change in the future as models improve. Therefore, the projection results for late twenty-first century conditions are presented as possible projections based on the “state-of-the-art” of present climate modeling. First, various criteria were computed making it possible to evaluate the models’ skills in simulating late twentieth century precipitation over continental areas as well as their divergence in projecting climate change conditions. Despite the relatively poor skill of most of the climate models in simulating present-day large scale precipitation patterns, we identified two types of models: the climate models with moderate-to-normal (i.e., close to observations) precipitation amplitudes over the Amazonian basin; and the climate models with a low precipitation in that region and too high a precipitation on the equatorial Pacific coast. Under SRES A2 greenhouse gas forcing, the neural network simulates an increase in precipitation over the La Plata basin coherent with the mean model ensemble projection. Over the Amazonian basin, a decrease in precipitation is projected. However, the models strongly diverge, and the neural network was found to give more weight to models, which better simulate present-day climate conditions. In the southern tip of the continent, the models poorly simulate present-day climate. However, they display a fairly good convergence when simulating climate change response with a weak increase south of 45°S and a decrease in Chile between 30 and 45°S. Other scenarios (A1B and B1) strongly resemble the SRES A2 trends but with weaker amplitudes.     

Displacements of the earth's surface in seismic regions

am amuuma u mua u nmu a a¶rt;uau nu, an u aua u -a.     

Etmopterus alphus n. sp.: a new lanternshark (Squaliformes: Etmopteridae) from the south-western Indian Ocean

A new species of lanternshark, Etmopterus alphus (Squaliformes: Etmopteridae), is described from the south-western Indian Ocean. The new species resembles other members of the ‘Etmopterus lucifer’ clade in having linear rows of dermal denticles and most closely resembles E. molleri from the south-western Pacific. The new species is fairly common along the upper continental slopes off central Mozambique, at depths between 472 and 558?m, and is also found on the southern Madagascar Ridge in 650–792?m depth. It can be distinguished from other members of the E. lucifer clade by a combination of characteristics, including arrangement of flank and caudal markings, dimension of flank markings and shape, size and arrangement of dermal denticles along the body. Molecular analysis further supports the distinction of E. alphus from other members of the E. lucifer clade.     

Shear Strength Development with Burial in Eel River Margin Slope Sediments

As part of the STRATAFORM project, a series of cores were obtained from the Eel River Margin area of Eureka, California. The geotechnical analysis of intact specimens and of reconstituted samples provides some insight on the development of shear strength with burial. The results show the effect of bioturbation in the early part of the lifetime of a sediment. SEDCON tests were used to proposed various relationships which help predict the changes in density, liquidity index, and strength as a function of depth. These relationships are found useful from near the water sediment-interface down to a depth of at least 400 m in the sediment column.     

Ecosystem modelling in the southern Benguela: comparisons of Atlantis,Ecopath with Ecosim,and OSMOSE under fishing scenarios

Ecosystem-based management of marine fisheries requires the use of simulation modelling to investigate the system-level impact of candidate fisheries management strategies. However, testing of fundamental assumptions such as system structure or process formulations is rarely done. In this study, we compare the output of three different ecosystem models (Atlantis, Ecopath with Ecosim, and OSMOSE) applied to the same ecosystem (the southern Benguela), to explore which ecosystem effects of fishing are most sensitive to model uncertainty. We subjected the models to two contrasting fishing pressure scenarios, applying high fishing pressure to either small pelagic fish or to adult hake. We compared the resulting model behaviour at a system level, and also at the level of model groups. We analysed the outputs in terms of various commonly used ecosystem indicators, and found some similarities in the overall behaviour of the models, despite major differences in model formulation and assumptions. Direction of change in system-level indicators was consistent for all models under the hake pressure scenario, although discrepancies emerged under the small-pelagic-fish scenario. Studying biomass response of individual model groups was key to understanding more integrated system-level metrics. All three models are based on existing knowledge of the system, and the convergence of model results increases confidence in the robustness of the model outputs. Points of divergence in the model results suggest important areas of future study. The use of feeding guilds to provide indicators for fish species at an aggregated level was explored, and proved to be an interesting alternative to aggregation by trophic level.     

Changes in recreational shore anglers’ attitudes towards,and awareness of,linefish management along the KwaZulu-Natal coast,South Africa

Management of recreational fisheries cannot be based on biological and stock assessment data alone but needs to include appropriate social aspects (including knowledge, attitudes and behaviour) of anglers within the fishery. The primary purpose of this study was to evaluate and complement existing recreational fisheries research, through the analysis of demographic and psychographic angler attributes collected from two independent, shore-based snapshot monitoring surveys conducted on the KwaZulu-Natal (KZN) coastline of South Africa, in 1994–1996 and 2009–2010. Results show significant changes between the two survey events in the demographics of anglers (including ethnic composition, age distribution, years of fishing experience and employment status) participating in the KZN shore-based linefishery. Traditional management regulations (minimum size limits, daily bag limits and closed seasons), though appearing to have support, have had limited effectiveness, based on the increased levels of admitted non-compliance and poor knowledge of regulations for target species. Anglers in both surveys believed that catches had declined over the years, with overfishing being the most common reason given. The results are discussed in the context of changing management practices in the KZN recreational shore-based linefishery. The implications of changes in fisheries management policies and responsibilities along the KZN coast are highlighted.