scholarly journals Development of Tailored Early Warning Agromet Advisories for Farmers in Zambia, Indonesia, and South Africa

2021 ◽  
Vol 3 ◽  
Author(s):  
Sue Walker
Keyword(s):  
South Africa ◽  
Local Knowledge ◽  
Early Warning ◽  
Farming Systems ◽  
Mobile App ◽  
Seasonal Forecasts ◽  
Climate Services ◽  
Science Field ◽  
Weather Forecasts ◽  
Advisory Systems

Farmers do not often use climate and weather information on a regular basis, as the specific influence of weather parameters on farm-level decision making is not well-known. Agromet advisories are needed for local farming systems. Effective agrometeorological advisory systems, need tailored agricultural weather forecasts, and two-way communication. Transdisciplinary teams together with farmers can co-develop early warning Agromet advisory systems to address farmers' needs. Three examples of Agromet advisories are discussed- CAPES in Zambia, Science Field Shops in Indonesia, and the AgriCloud mobile App in South Africa. Community Agrometeorological Participatory Extension Service (CAPES) began in Monze, Zambia to communicate seasonal climate forecasts to farmers through researchers and extension interactions. Participatory groups collected spatial and temporal data about local farming systems to highlight opportunities. Communication methods used were local radio, farmers' days, trials, with farm visits. CAPES resulted in lifelong learning about climate and co-development of tailored Agromet advisories to improve climate resilience. In Science Field Shops (SFS) groups of Indonesian farmers meet experts regularly to exchange information about climate and farming activities. Farmers measure rainfall and observe their agroecological systems each day. At monthly meetings, the seasonal forecasts are discussed using dialogue-discussion methods. Agrometeorological learning is trans-disciplinary through interaction between anthropologists, agrometeorologist, and extension personnel. SFS includes eight climate services that empower farmers to address challenges and sustain their productivity. AgriCloud is an online weather-based agricultural advisory system enriching weather forecasts with agricultural information and local knowledge. Real-time overviews and warnings are tailored to farmer's needs. AgriCloud provides farmers, extension staff, and advisors daily updated weather-related farm-specific advice in 11 South African official languages. AgriCloud is available as an android mobile App, or API to use via a platform. These examples illustrate the use of weather forecasting together with tailored forecasts and communication systems to deliver Agromet advisories, showing different aspects of the incorporation of local knowledge in co-developing advisories for the farmers. In the future, various combinations can be used around the world when co-developing with the farmers.

scholarly journals REVIEW OF SEASONAL CLIMATE FORECASTING FOR AGRICULTURE IN SUB-SAHARAN AFRICA

2011 ◽  
Vol 47 (2) ◽  
pp. 205-240 ◽  
Author(s):  
JAMES W. HANSEN ◽  
SIMON J. MASON ◽  
LIQIANG SUN ◽  
ARAME TALL
Keyword(s):  
Smallholder Farmers ◽  
Seasonal Forecast ◽  
Sub Saharan Africa ◽  
Seasonal Forecasting ◽  
Seasonal Forecasts ◽  
Climate Services ◽  
Seasonal Climate ◽  
Climate Forecasting ◽  
Probabilistic Forecasts ◽  
Sub Saharan

SUMMARYWe review the use and value of seasonal climate forecasting for agriculture in sub-Saharan Africa (SSA), with a view to understanding and exploiting opportunities to realize more of its potential benefits. Interaction between the atmosphere and underlying oceans provides the basis for probabilistic forecasts of climate conditions at a seasonal lead-time, including during cropping seasons in parts of SSA. Regional climate outlook forums (RCOF) and national meteorological services (NMS) have been at the forefront of efforts to provide forecast information for agriculture. A survey showed that African NMS often go well beyond the RCOF process to improve seasonal forecast information and disseminate it to the agricultural sector. Evidence from a combination of understanding of how climatic uncertainty impacts agriculture, model-based ex-ante analyses, subjective expressions of demand or value, and the few well-documented evaluations of actual use and resulting benefit suggests that seasonal forecasts may have considerable potential to improve agricultural management and rural livelihoods. However, constraints related to legitimacy, salience, access, understanding, capacity to respond and data scarcity have so far limited the widespread use and benefit from seasonal prediction among smallholder farmers. Those constraints that reflect inadequate information products, policies or institutional process can potentially be overcome. Additional opportunities to benefit rural communities come from expanding the use of seasonal forecast information for coordinating input and credit supply, food crisis management, trade and agricultural insurance. The surge of activity surrounding seasonal forecasting in SSA following the 1997/98 El Niño has waned in recent years, but emerging initiatives, such as the Global Framework for Climate Services and ClimDev-Africa, are poised to reinvigorate support for seasonal forecast information services for agriculture. We conclude with a discussion of institutional and policy changes that we believe will greatly enhance the benefits of seasonal forecasting to agriculture in SSA.

Skill assessment of post-processing methods for ECMWF SEAS5 seasonal forecasts over Europe

2021 ◽  
Author(s):  
Alice Crespi ◽  
Marcello Petitta ◽  
Lucas Grigis ◽  
Paola Marson ◽  
Jean-Michel Soubeyroux ◽  
...  
Keyword(s):  
Large Scale ◽  
Production Management ◽  
Spatial Scales ◽  
Research Field ◽  
Skill Assessment ◽  
Post Processing ◽  
Seasonal Forecasts ◽  
Climate Services ◽  
Local Climate ◽  
Climate Conditions

<p>Seasonal forecasts provide information on climate conditions several months ahead and therefore they could represent a valuable support for decision making, warning systems as well as for the optimization of industry and energy sectors. However, forecast systems can be affected by systematic biases and have horizontal resolutions which are typically coarser than the spatial scales of the practical applications. For this reason, the reliability of forecasts needs to be carefully assessed before applying and interpreting them for specific applications. In addition, the use of post-processing approaches is recommended in order to improve the representativeness of the large-scale predictions of regional and local climate conditions. The development and evaluation downscaling and bias-correction procedures aiming at improving the skills of the forecasts and the quality of derived climate services is currently an open research field. In this context, we evaluated the skills of ECMWF SEAS5 forecasts of monthly mean temperature, total precipitation and wind speed over Europe and we assessed the skill improvements of calibrated predictions.</p><p>For the calibration, we combined a bilinear interpolation and a quantile mapping approach to obtain corrected monthly forecasts on a 0.25°x0.25° grid from the original 1°x1° values. The forecasts were corrected against the reference ERA5 reanalysis over the hindcast period 1993–2016. The processed forecasts were compared over the same domain and period with another calibrated set of ECMWF SEAS5 forecasts obtained by the ADAMONT statistical method.</p><p>The skill assessment was performed by means of both deterministic and probabilistic verification metrics evaluated over seasonal forecasted aggregations for the first lead time. Greater skills of the forecast systems in Europe were generally observed in spring and summer, especially for temperature, with a spatial distribution varying with the seasons. The calibration was proved to effectively correct the model biases for all variables, however the metrics not accounting for bias did not show significant improvements in most cases, and in some areas and seasons even small degradations in skills were observed.</p><p>The presented study supported the activities of the H2020 European project SECLI-FIRM on the improvement of the seasonal forecast applicability for energy production, management and assessment.</p>

scholarly journals Opportunities for Mobile App–Based Adherence Support for Children With Tuberculosis in South Africa

10.2196/19154 ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. e19154
Author(s):  
Rachel M Morse ◽  
Hanlie Myburgh ◽  
David Reubi ◽  
Ava E Archey ◽  
Leletu Busakwe ◽  
...  
Keyword(s):  
South Africa ◽  
Treatment Adherence ◽  
Mobile Apps ◽  
Mobile App ◽  
Tuberculosis Treatment ◽  
Mobile Technologies ◽  
Patient Treatment ◽  
Pilot Studies ◽  
Adherence Support ◽  
Positive Treatment

Tuberculosis is the number one infectious cause of death globally. Young children, generally those younger than 5 years, are at the highest risk of progressing from tuberculosis infection to tuberculosis disease and of developing the most severe forms of tuberculosis. Most current tuberculosis drug formulations have poor acceptability among children and require consistent adherence for prolonged periods of time. These challenges complicate children’s adherence to treatment and caregivers’ daily administration of the drugs. Rapid developments in mobile technologies and apps present opportunities for using widely available technology to support national tuberculosis programs and patient treatment adherence. Pilot studies have demonstrated that mobile apps are a feasible and acceptable means of enhancing children’s treatment adherence for other chronic conditions. Despite this, no mobile apps that aim to promote adherence to tuberculosis treatment have been developed for children. In this paper, we draw on our experiences carrying out research in clinical pediatric tuberculosis studies in South Africa. We present hypothetical scenarios of children’s adherence to tuberculosis medication to suggest priorities for behavioral and educational strategies that a mobile app could incorporate to address some of the adherence support gaps faced by children diagnosed with tuberculosis. We argue that a mobile app has the potential to lessen some of the negative experiences that children associate with taking tuberculosis treatment and to facilitate a more positive treatment adherence experience for children and their caregivers.

scholarly journals Seasonal forecasts of the Saharan heat low characteristics: a multi-model assessment

2021 ◽  
Vol 2 (3) ◽  
pp. 893-912
Author(s):  
Cedric G. Ngoungue Langue ◽  
Christophe Lavaysse ◽  
Mathieu Vrac ◽  
Philippe Peyrillé ◽  
Cyrille Flamant
Keyword(s):  
West African Monsoon ◽  
Seasonal Forecast ◽  
Lead Times ◽  
Model Assessment ◽  
Seasonal Forecasts ◽  
Weather Forecasts ◽  
Modes Of Variability ◽  
Forecast Models ◽  
Sahel Region ◽  
Heat Low

Abstract. The Saharan heat low (SHL) is a key component of the West African Monsoon system at the synoptic scale and a driver of summertime precipitation over the Sahel region. Therefore, accurate seasonal precipitation forecasts rely in part on a proper representation of the SHL characteristics in seasonal forecast models. This is investigated using the latest versions of two seasonal forecast systems namely the SEAS5 and MF7 systems from the European Center of Medium-Range Weather Forecasts (ECMWF) and Météo-France respectively. The SHL characteristics in the seasonal forecast models are assessed based on a comparison with the fifth ECMWF Reanalysis (ERA5) for the period 1993–2016. The analysis of the modes of variability shows that the seasonal forecast models have issues with the timing and the intensity of the SHL pulsations when compared to ERA5. SEAS5 and MF7 show a cool bias centered on the Sahara and a warm bias located in the eastern part of the Sahara respectively. Both models tend to underestimate the interannual variability in the SHL. Large discrepancies are found in the representation of extremes SHL events in the seasonal forecast models. These results are not linked to our choice of ERA5 as a reference, for we show robust coherence and high correlation between ERA5 and the Modern-Era Retrospective analysis for Research and Applications (MERRA). The use of statistical bias correction methods significantly reduces the bias in the seasonal forecast models and improves the yearly distribution of the SHL and the forecast scores. The results highlight the capacity of the models to represent the intraseasonal pulsations (the so-called east–west phases) of the SHL. We notice an overestimation of the occurrence of the SHL east phases in the models (SEAS5, MF7), while the SHL west phases are much better represented in MF7. In spite of an improvement in prediction score, the SHL-related forecast skills of the seasonal forecast models remain weak for specific variations for lead times beyond 1 month, requiring some adaptations. Moreover, the models show predictive skills at an intraseasonal timescale for shorter lead times.

Determination of seasonal forecast skill in identifying extreme events of temperature, wind speed, and SPI

2021 ◽  
Author(s):  
Massimiliano Palma ◽  
Franco Catalano ◽  
Irene Cionni ◽  
Marcello Petitta
Keyword(s):  
Climate Change ◽  
Wind Speed ◽  
Climate Variability ◽  
Extreme Events ◽  
Bias Correction ◽  
Skill Score ◽  
Seasonal Forecast ◽  
Energy Sector ◽  
Seasonal Forecasts ◽  
Weather Forecasts

<p>Renewable energy is the fastest-growing source of electricity globally, but climate variability and impacting events affecting the potential productivity of plants are obstacles to its integration and planning. Knowing a few months in advance the productivity of plants and the impact of extreme events on productivity and infrastructure can help operators and policymakers make the energy sector more resilient to climate variability, promoting the deployment of renewable energy while maintaining energy security.</p><p>The energy sector already uses weather forecasts up to 15 days for plant management; beyond this time horizon, climatologies are routinely used. This approach has inherent weaknesses, including the inability to predict extreme events, the prediction of which is extremely useful to decision-makers. Information on seasonal climate variability obtained through climate forecasts can be of considerable benefit in decision-making processes. The Climate Data Store of the Copernicus Climate Change Service (C3S) provides seasonal forecasts and a common period of retrospective simulations (hindcasts) with equal spatial temporal resolution for simulations from 5 European forecast centres (European Centre for Medium-Range Weather Forecasts (ECMWF), Deutscher Wetterdienst (DWD), Meteo France (MF), UK Met Office (UKMO) and Euro-Mediterranean Centre on Climate Change (CMCC)), one US forecasting centre (NCEP) plus the Japan Meteorological Agency (JMA) model.</p><p>In this work, we analyse the skill and the accuracy of a subset of the operational seasonal forecasts provided by Copernicus C3S, focusing on three relevant essential climate variables for the energy sector: temperature (t2m), wind speed (sfcWind, relevant to the wind energy production), and precipitation. The latter has been analysed by taking the Standard Precipitation Index (SPI) into account.</p><p>First, the methodologies for bias correction have been defined. Subsequently, the reliability of the forecasts has been assessed using appropriate reliability indicators based on comparison with ERA5 reanalysis dataset. The hindcasts cover the period 1993-2017. For each of the variables considered, we evaluated the seasonal averages based on monthly means for two seasons: winter (DJF) and summer (JJA). Data have been bias corrected following two methodologies, one based on the application of a variance inflation technique to ensure the correction of the bias and the correspondence of variance between forecast and observation; the other based on the correction of the bias, the overall forecast variance and the ensemble spread as described in Doblas-Reyes et al. (2005).</p><p>Predictive ability has been assessed by calculating binary (Brier Skill Score, BSS hereafter, and Ranked Probability Skill Score, RPSS hereafter) and continuous (Continuous Ranked Probability Skill Score, CRPSS hereafter) scores. Forecast performance has been assessed using ERA 5 reanalysis as pseudo-observations. </p><p>In this work we discuss the results obtained with different bias correction techniques highlighting the outcomes obtained analyzing the BSS for the first and the last terciles and the first and the last percentiles (10th and 90th). This analysis has the goal to identify the regions in which the seasonal forecast can be used to identify potential extreme events.</p>

Incremental Learning and Gradual Changes

2017 ◽  
pp. 1454-1487
Author(s):  
Yunita Triwardani Winarto ◽  
Kees/Cornelis Johan Stigter
Keyword(s):  
Climate Change ◽  
Learning Process ◽  
Incremental Learning ◽  
Weather Conditions ◽  
State Policies ◽  
Coping Mechanism ◽  
Climate Services ◽  
Science Field ◽  
Environmental Consequences

The environmental consequences of climate change in agriculture have been degrading farmers' livelihood and welfare because of their inability to cope with the unusual risks and livelihood crises due to climate change. However, state policies addressing farmers' needs to develop their coping mechanism towards the unusual consequences of climate change have not been defined appropriately. To continue relying on their conventional ways of farming would not effectively help them to survive in the midst of unusual weather conditions. A long-term educational commitment to improve farmers' agrometeorological learning is urgent. The authors present their inter- and trans-disciplinary collaborative works in providing climate services to farmers through Science Field Shops. Improving farmers' anticipation capability has been carried out through various means of learning. The authors describe the incremental learning process and the gradual changes the farmers in Indramayu Regency have gone through since 2010.

scholarly journals The potential value of seasonal forecasts in a changing climate in southern Africa

2014 ◽  
Vol 18 (4) ◽  
pp. 1525-1538 ◽  
Author(s):  
H. C. Winsemius ◽  
E. Dutra ◽  
F. A. Engelbrecht ◽  
E. Archer Van Garderen ◽  
F. Wetterhall ◽  
...  
Keyword(s):  
Climate Change ◽  
Heat Stress ◽  
Southern Africa ◽  
Weather Conditions ◽  
Stress Conditions ◽  
Mitigation Measures ◽  
Seasonal Forecasts ◽  
Weather Forecasts ◽  
Dry Spells ◽  
The Future

Abstract. Subsistence farming in southern Africa is vulnerable to extreme weather conditions. The yield of rain-fed agriculture depends largely on rainfall-related factors such as total seasonal rainfall, anomalous onsets and lengths of the rainy season and the frequency of occurrence of dry spells. Livestock, in turn, may be seriously impacted by climatic stress with, for example, exceptionally hot days, affecting condition, reproduction, vulnerability to pests and pathogens and, ultimately, morbidity and mortality. Climate change may affect the frequency and severity of extreme weather conditions, impacting on the success of subsistence farming. A potentially interesting adaptation measure comprises the timely forecasting and warning of such extreme events, combined with mitigation measures that allow farmers to prepare for the event occurring. This paper investigates how the frequency of extreme events may change in the future due to climate change over southern Africa and, in more detail, the Limpopo Basin using a set of climate change projections from several regional climate model downscalings based on an extreme climate scenario. Furthermore, the paper assesses the predictability of these indicators by seasonal meteorological forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting system. The focus is on the frequency of dry spells as well as the frequency of heat stress conditions expressed in the temperature heat index. In areas where their frequency of occurrence increases in the future and predictability is found, seasonal forecasts will gain importance in the future, as they can more often lead to informed decision-making to implement mitigation measures. The multi-model climate projections suggest that the frequency of dry spells is not likely to increase substantially, whereas there is a clear and coherent signal among the models of an increase in the frequency of heat stress conditions by the end of the century. The skill analysis of the seasonal forecast system demonstrates that there is a potential to adapt to this change by utilizing the weather forecasts, given that both indicators can be skilfully predicted for the December–February season, at least 2 months ahead of the wet season. This is particularly the case for predicting above-normal and below-normal conditions. The frequency of heat stress conditions shows better predictability than the frequency of dry spells. Although results are promising for end users on the ground, forecasts alone are insufficient to ensure appropriate response. Sufficient support for appropriate measures must be in place, and forecasts must be communicated in a context-specific, accessible and understandable format.

People, Clouds, and Roots

2019 ◽  
Vol 14 (3) ◽  
pp. 236-250 ◽  
Author(s):  
Yunita T. Winarto ◽  
Sue Walker ◽  
Rhino Ariefiansyah
Keyword(s):  
Decision Making ◽  
Local Knowledge ◽  
Unintended Consequences ◽  
Empirical Knowledge ◽  
Science Field ◽  
Mind Set

Various studies reveal the paradox of farmers’ local knowledge. Farmers are equipped with traditional cosmology and detailed empirical knowledge of their agricultural habitats. However, these same knowledge frameworks seem to contribute to entrapping farmers in a mind-set that prevents them from understanding the diverse unintended consequences of changes in their environment. To avoid this, we utilize the learning arena of science field shops (SFSs) to help farmers better understand the relationships at work from the “clouds to the roots and in between”, and to address ongoing changes and vulnerabilities in the environment. This article seeks to explain the changes that occurred to farmers following the learning they acquired from SFSs and its impact on their anticipation and decision making.

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