scholarly journals Assessment of Knowledge Co-production in Adaptation to Rainfall Variability in Kitui South Sub-county, Kenya

2020 ◽  
pp. 87-97
Author(s):  
Morris M. Mwatu ◽  
Charles W. Recha ◽  
Kennedy N. Ondimu
Keyword(s):  
Rainfall Variability ◽  
Study Data ◽  
Climate Forecast ◽  
Seasonal Climate Forecast ◽  
Scientific Methods ◽  
Seasonal Climate ◽  
Qualitative And Quantitative ◽  
Climate Forecasting ◽  
Climate Forecasts ◽  
Household Heads

Aims: This study tried to investigate the extent of knowledge co-production between indigenous farmers and agricultural extension in dry lands. Study Design: The study adopted survey research design where both qualitative and quantitative approaches were used. Place and Duration of Study: The study was carried out in Kitui South sub-County in the semi-arid Southeastern Kenya. Data was collected between June 2019 and August 2019. Methodology: An enumerator-administered questionnaire was used to collect data from 311 household heads. Purposive and proportional sampling techniques were used to select households which participated in the study. Data was analyzed with the aid of SPSS Version 20. Percentages and proportions were used to establish instances of knowledge co-production between indigenous and modern scientific methods of farming. Results: The study established that all households used both indigenous and scientific methods of farming except in irrigation and crop harvesting methods. The highest co-production was between use of locally preserved seeds and use of modern seasonal climate forecast (71.4%), use of traditional seasonal climate forecasts and use of modern seasonal climate (64.6%) as well as use of traditional crop storage and use modern seasonal climate forecast (59.2%). Seasonal climate forecasting was the leading corresponding method of knowledge co-production in the study area. Conclusion: The study concludes that use of both indigenous and modern methods of farming can improve adaptation to rainfall variability. The study recommends access to adequate water to promote knowledge co-production on irrigation which was lacking yet very critical in dealing with rainfall variability in the study area.

scholarly journals Opportunities and challenges for seasonal climate forecasts to more effectively assist smallholder farming decisions

2020 ◽  
Vol 116 (1/2) ◽  
Author(s):  
Bright Chisadza ◽  
Abbyssinia Mushunje ◽  
Kenneth Nhundu ◽  
Ethel E. Phiri
Keyword(s):  
Decision Making ◽  
Smallholder Farmers ◽  
Smallholder Farming ◽  
Seasonal Climate Forecast ◽  
Seasonal Climate ◽  
Farm Planning ◽  
Climate Forecasting ◽  
Farm Production ◽  
Climate Forecasts ◽  
Challenges And Opportunities

The ability of smallholder farmers to utilise seasonal climate forecast (SCF) information in farm planning to reflect anticipated climate is a precursor to improved farm management. However, the integration of SCF by smallholder farmers into farm planning has been poor, partly because of the lack of forecast skill, lack of communication and inability to see the relevance of the SCFs for specific farming decisions. The relevance of seasonal climate forecasting in farming decisions can be enhanced through improved understanding of SCF from the smallholder farmers’ perspective. Studies that have been done of how smallholder farmers understand SCF and how the available SCFs influence smallholder farmers’ decisions are limited. Therefore, the objective of this paper was to review how smallholder farmers make decisions on farming practices based on SCFs and the challenges and opportunities thereof. The review shows that the majority of smallholder farmers in Africa make use of either scientific or indigenous knowledge climate forecasts and, in some cases, a combination of both. There are mixed results in the area of evaluating benefits of SCFs in decision-making and farm production. In some cases, the outcomes are positive, whereas in others they are difficult to quantify. Thus, the integration of SCFs into smallholder farmers’ decision-making is still a challenge. We recommend that significant work must be done to improve climate forecasts in terms of format, and spatial and temporal context in order for them to be more useful in influencing decision-making by smallholder farmers.

scholarly journals Application of the NMME in the Development of a New Regional Seasonal Climate Forecast Tool

2017 ◽  
Vol 98 (3) ◽  
pp. 555-564 ◽  
Author(s):  
Rebecca A. Bolinger ◽  
Andrew D. Gronewold ◽  
Keith Kompoltowicz ◽  
Lauren M. Fry
Keyword(s):  
North American ◽  
Climate Prediction ◽  
Successful Implementation ◽  
Climate Forecast ◽  
Seasonal Climate Forecast ◽  
Seasonal Climate ◽  
Continental Scale ◽  
The North ◽  
Climate Forecasts ◽  
Seasonal Climate Forecasts

ABSTRACT The National Oceanic and Atmospheric Administration’s Climate Prediction Center (CPC) provides access to a suite of real-time monthly climate forecasts that compose the North American Multi-Model Ensemble (NMME) in an attempt to meet the increasing demands for monthly to seasonal climate prediction. While the North American and global map-based forecasts provided by CPC are informative on a broad or continental scale, operational and decision-making institutions need products with a much more specific regional focus. To address this need, we developed a Region-Specific Seasonal Climate Forecast (RSCF–NMME) tool by combining NMME forecasts with regional climatological data. The RSCF–NMME automatically downloads and archives data and is displayed via a dynamic web-based graphical user interface. The tool has been applied to the Great Lakes region and utilized as part of operational water-level forecasting procedures by the U.S. Army Corps of Engineers, Detroit District (USACE-Detroit). Evaluation of the tool, compared with seasonal climate forecasts released by CPC, shows that the tool can provide additional useful information to users and overcomes some of the limitations of the CPC forecasts. The RSCF–NMME delivers details about a specific region’s climate, verification observations, and the ability to view different model forecasts. With its successful implementation within an operational environment, the tool has proven beneficial and thus set a precedent for expansion to other regions where there is a demand for region-specific seasonal climate forecasts.

scholarly journals Systematic global evaluation of accuracy of seasonal climateforecasts for monthly precipitation of JMA/MRI-CPS2 bycomparing with a statistical system using climate indices

2021 ◽  
Author(s):  
Yuji Masutomi ◽  
Toshichika Iizumi ◽  
Key Oyoshi ◽  
Nobuyuki Kayaba ◽  
Wonsik Kim ◽  
...  
Keyword(s):  
Japan Meteorological Agency ◽  
Climate Indices ◽  
Global Evaluation ◽  
Monthly Precipitation ◽  
Climate Forecast ◽  
Statistical System ◽  
Slow Dynamics ◽  
Seasonal Climate Forecast ◽  
Seasonal Climate

Abstract. In this study, we aimed to evaluate the monthly precipitation forecasts of JMA/MRI-CPS2, a global dynamical seasonal climate forecast (Dyn-SCF) system operated in the Japan Meteorological Agency, by comparing them with the forecasts of a statistical SCF (St-SCF) system using climate indices systematically and globally. Accordingly, we developed a new global St-SCF system using 18 climate indices and compared the monthly precipitation of this system with those of JMA/MRI-CPS2. Consequently, it was found that JMA/MRI-CPS2 forecasts are superior to St-SCFs around the equator (10° S–10° N) even for six-month lead forecasts. For one-month lead forecasts, the accuracy of JMA/MRI-CPS2 forecasts was higher than that of St-SCFs when viewed globally. In contrast, for forecasts made two months or longer in advance, St-SCFs had an advantage in global forecasts. In addition to evaluating the accuracy of JMA/MRI-CPS2 forecasts, the slow dynamics of the ocean and atmosphere, not reproduced by the JMA/MRI-CPS2 system, were determined by comparing the evaluations, and it was concluded that this could contribute to improving Dyn-SCF systems.

scholarly journals Enabling Conditions for Improved Use of Seasonal Climate Forecast in Arid and Semi-Arid Baringo County—Kenya

OALib ◽  
2017 ◽  
Vol 04 (08) ◽  
pp. 1-15 ◽  
Author(s):  
Richard Ochieng ◽  
Charles Recha ◽  
Bockline Omedo Bebe
Keyword(s):  
Climate Forecast ◽  
Seasonal Climate Forecast ◽  
Seasonal Climate ◽  
Semi Arid
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