Agro-meteorological risks to maize production in Tanzania: sensitivity of an adapted water requirements satisfaction index (WRSI) model to rainfall

The water requirements satisfaction index (WRSI) – a simplified crop water stress model – is widely used in drought and famine early warning systems, as well as in agro-meteorological risk management instruments such as crop insurance. We developed an adapted WRSI model, as introduced here, to characterise the impact of using different rainfall input datasets, ARC2, CHIRPS, and TAMSAT, on key WRSI model parameters and outputs. Results from our analyses indicate that CHIRPS best captures seasonal rainfall characteristics such as season onset and duration, which are critical for the WRSI model. Additionally, we consider planting scenarios for short-, medium-, and long-growing cycle maize and compare simulated WRSI and model outputs against reported yield at the national level for maize-growing areas in Tan- zania. We find that over half of the variability in yield is explained by water stress when the CHIRPS dataset is used in the WRSI model (R2 = 0.52- 0.61 for maize varieties of 120-160 days growing length). Overall, CHIRPS and TAMSAT show highest skill (R2 = 0.46-0.55 and 0.44-0.58, respectively) in capturing country-level crop yield losses related to seasonal soil moisture deficit, which is critical for drought early warning and agro-meteorological risk applications

Evaluation of CHIRPS rainfall estimates over Iran

The Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) dataset, first released in 2014, is a high-resolution blended rainfall product with quasi-global coverage that has not been previously evaluated over Iran. Here, we assess the performance of the CHIRPS rainfall estimates against ground-based rainfall observations across Iran over the time period from 2005 to 2014 inclusive. Results show that CHIRPS’ performance is better over areas and during the months of predominantly convective precipitation with highest correlations in the southern coastal lowlands characterized by heavy rains from convective origin. Correlations are stronger with variables such as altitude, particularly alongside coastal regions in the north and south, where surface water produces more moisture in the atmosphere. Results of pairwise comparison statistics and categorical skill scores reveal the influence of altitude and precipitation amount, while categorical skill metrics vary more with changes in precipitation amount than with latitudinal or longitudinal changes

Outan: An On-Head System for Driving micro-LED Arrays Implanted in Freely Moving Mice

In the intact brain, neural activity can be recorded using sensing electrodes and manipulated using light stimulation. Silicon probes with integrated electrodes and micro-LEDs enable the detection and control of neural activity using a single implanted device. Miniaturized solutions for recordings from small freely moving animals are commercially available, but stimulation is driven by large, stationary current sources. We designed and fabricated a current source chip and integrated it into a headstage PCB that weighs 1.37 g. The proposed system provides 10-bit resolution current control for 32 channels, driving micro-LEDs with up to 4.6 V and sourcing up to 0.9 mA at a refresh rate of 5 kHz per channel. When calibrated against a micro-LED probe, the system allows linear control of light output power, up to 10 micro-W per micro-LED. To demonstrate the capabilities of the system, synthetic sequences of neural spiking activity were produced by driving multiple micro-LEDs implanted in the hippocampal CA1 area of a freely moving mouse. The high spatial, temporal, and amplitude resolution of the system provides a rich variety of stimulation patterns. Combined with commercially available sampling headstages, the system provides an easy to use back-end, fully utilizing the bi-directional potential of integrated opto-electronic arrays.Comment: 11 pages, 9 figure

Exploiting satellite-based rainfall for weather index insurance: the challenges of spatial and temporal aggregation

Lack of access to insurance exacerbates the impact of climate variability on smallholder famers in Africa. Unlike traditional insurance, which compensates proven agricultural losses, weather index insurance (WII) pays out in the event that a weather index is breached. In principle, WII could be provided to farmers throughout Africa. There are two data-related hurdles to this. First, most farmers do not live close enough to a rain gauge with sufficiently long record of observations. Second, mismatches between weather indices and yield may expose farmers to uncompensated losses, and insurers to unfair payouts – a phenomenon known as basis risk. In essence, basis risk results from complexities in the progression from meteorological drought (rainfall deficit) to agricultural drought (low soil moisture). In this study, we use a land-surface model to describe the transition from meteorological to agricultural drought. We demonstrate that spatial and temporal aggregation of rainfall results in a clearer link with soil moisture, and hence a reduction in basis risk. We then use an advanced statistical method to show how optimal aggregation of satellite-based rainfall estimates can reduce basis risk, enabling remotely sensed data to be utilized robustly for WII

Облачные вычисления в Интернете: краткий экскурс в Центр компьютерного моделирования

The conception of Cloud Computing in Internet is presented on the base of the Computer Simulation Center.На базе Центра компьютерного моделирования кратко представлена концепция облачных вычислений в Интернет

Monitoring drought in Ghana using TAMSAT-ALERT: a new decision support system

Approximately 886 million people in Africa rely on agriculture as their main means of survival. They are therefore susceptible to changes in seasonal rains from year to year that can result in agricultural drought. Agricultural drought is determined by low soil moisture content. Soil moisture responds to rainfall, but also depends on many other factors, including the soil characteristics and, crucially, on the past soil moisture. Here we demonstrate that predictive skill can be gained from knowledge of the current state of the land surface – how wet or dry the soil is – as the growing season evolves. This skill arises from the land surface memory – the soil moisture content at a particular time depends to a large extent on the historical soil moisture. By forcing a land surface model with observed data up to a ‘present day’ and then forward in time with climatological data (to represent the range of possible future conditions) we show that it is possible to be confident of an ensuing agricultural drought several weeks before the end of the growing season. This system is illustrated using results from an operational trial for Tamale in northern Ghana

МОДИФІКОВАНІ ПОЛІАМІДНІ МАТЕРІАЛИ З ПОНИЖЕНОЮ ЗДАТНІСТЮ ДО СТАТИЧНОЇ ЕЛЕКТРИЗАЦІЇ

In the article provides results of researches of properties of a film on the basis PA-6 modified by polyvinylpirrolidone (PVP) in intercommunication with their molecular structure. It is determined that PVP substantially reduces capacity for static electrization of polyamide film materials during their production on the technological equipment, increases physical-mechanical and thermal descriptions in the result of thermodynamic compatibility of components. The structural features are investigated and temperatures of phase transfer in mixtures with the various content of PVP are explored.В статті наведено результати досліджень властивостей плівок на основі ПА-6 модифікованого полівінілпіролідоном (ПВП) у взаємозв’язку з їх надмолекулярною структурою. Встановлено, що ПВП суттєво знижує здатність до статичної електризації поліамідних плівкових матеріалів під час їх виготовлення на технологічному обладнанні, підвищує їх фізико-механічні та теплофізичні характеристики внаслідок термодинамічної сумісності компонентів. Досліджені структурні особливості та визначені температури фазових переходів у сумішах з різним вмістом ПВП

The 30-year TAMSAT African rainfall climatology and time-series (TARCAT) dataset

African societies are dependent on rainfall for agricultural and other water-dependent activities, yet rainfall is extremely variable in both space and time and reoccurring water shocks, such as drought, can have considerable social and economic impacts. To help improve our knowledge of the rainfall climate, we have constructed a 30-year (1983–2012), temporally consistent rainfall dataset for Africa known as TARCAT (TAMSAT African Rainfall Climatology And Time-series) using archived Meteosat thermal infra-red (TIR) imagery, calibrated against rain gauge records collated from numerous African agencies. TARCAT has been produced at 10-day (dekad) scale at a spatial resolution of 0.0375°. An intercomparison of TARCAT from 1983 to 2010 with six long-term precipitation datasets indicates that TARCAT replicates the spatial and seasonal rainfall patterns and interannual variability well, with correlation coefficients of 0.85 and 0.70 with the Climate Research Unit (CRU) and Global Precipitation Climatology Centre (GPCC) gridded-gauge analyses respectively in the interannual variability of the Africa-wide mean monthly rainfall. The design of the algorithm for drought monitoring leads to TARCAT underestimating the Africa-wide mean annual rainfall on average by −0.37 mm day−1 (21%) compared to other datasets. As the TARCAT rainfall estimates are historically calibrated across large climatically homogeneous regions, the data can provide users with robust estimates of climate related risk, even in regions where gauge records are inconsistent in time