Perception and Use of Climate Forecast Information Amongst Smallholder Farmers in Semi-Arid Kenya

Shisanya, C.A.; Recha, Charles W.; Makokha, George L.; Kinuthia, Ruth

doi:10.3923/ajaps.2008.123.135

Cited by 22 publications

(11 citation statements)

References 13 publications

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“…The increase in ten-year moving CV of SR rainfall from 30% in 1980s to 50% during 2000–10 is a major change with a potential to impact the productive potential of many crops. Assorted studies have cited similar patterns in seasonal rainfall over lower eastern Kenya [ 48 – 51 ]. The study has also established that surface temperatures are warming at the rate of up to 0.03°C/decade.…”

Section: Discussionmentioning

confidence: 81%

Simulating adaptation strategies to offset potential impacts of climate variability and change on maize yields in Embu County, Kenya

et al. 2020

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In this study, we assessed the possible impacts of climate variability and change on growth and performance of maize using multi-climate, multi-crop model approaches built on Agricultural Model Intercomparison and Improvement Project (AgMIP) protocols in five different agro-ecological zones (AEZs) of Embu County in Kenya and under different management systems. Adaptation strategies were developed that are locally relevant by identifying a set of technologies that help to offset potential impacts of climate change on maize yields. Impacts and adaptation options were evaluated using projections by 20 Coupled Model Intercomparison Project—Phase 5 (CMIP5) climate models under two representative concentration pathways (RCPs) 4.5 and 8.5. Two widely used crop simulation models, Agricultural Production Systems Simulator (APSIM) and Decision Support System for Agrotechnology Transfer (DSSAT) was used to simulate the potential impacts of climate change on maize. Results showed that 20 CMIP5 models are consistent in their projections of increased surface temperatures with different magnitude. Projections by HadGEM2-CC, HadGEM2-ES, and MIROC-ESM tend to be higher than the rest of 17 CMIP5 climate models under both emission scenarios. The projected increase in minimum temperature (Tmin) which ranged between 2.7 and 5.8°C is higher than the increase in maximum temperature (Tmax) that varied between 2.2 and 4.8°C by end century under RCP 8.5. Future projections in rainfall are less certain with high variability projections by GFDL-ESM2G, MIROC5, and NorESM1-M suggest 8 to 25% decline in rainfall, while CanESM2, IPSL-CM5A-MR and BNU-ESM suggested more than 85% increase in rainfall under RCP 8.5 by end of 21st century. Impacts of current and future climatic conditions on maize yields varied depending on the AEZs, soil type, crop management and climate change scenario. Impacts are largely negative in the low potential AEZs such as Lower Midlands (LM4 and LM5) compared with the high potential AEZs Upper Midlands (UM2 and UM3). However, impacts of climate change are largely positive across all AEZs and management conditions when CO2 fertilization is included. Using the differential impacts of climate change, a strategy to adapt maize cultivation to climate change in all the five AEZs was identified by consolidating those practices that contributed to increased yields under climate change. We consider this approach as more appropriate to identify operational adaptation strategies using readily available technologies that contribute positively under both current and future climatic conditions. This approach when adopted in strategic manner will also contribute to further strengthen the development of adaptation strategies at national and local levels. The methods and tools validated and applied in this assessment allowed estimating possible impacts of climate change and adaptation strategies which can provide valuable insights and guidance for adaptation planning.

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Section: Discussionmentioning

confidence: 81%

Simulating adaptation strategies to offset potential impacts of climate variability and change on maize yields in Embu County, Kenya

et al. 2020

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“…While there is a higher proportion of users of SF in Area 1, in both areas the use of IF dominates (Figure 3), which is similar to earlier findings. 2,26 This was despite the high confidence users have in SF in the low use area (Area 2), which is also indicated elsewhere in Kenya. 26 In terms of credibility, salience, and legitimacy of the forecasts, not much has changed in 10 years.…”

Section: Discussionmentioning

confidence: 80%

“…2,26 This was despite the high confidence users have in SF in the low use area (Area 2), which is also indicated elsewhere in Kenya. 26 In terms of credibility, salience, and legitimacy of the forecasts, not much has changed in 10 years. While farmers listen to SF and some use this in decision making, they do not always trust the forecasts, indicating lack of credibility, or farmers believe that the forecasts are not relevant to them, indicating lack 16 In addition, farmers still struggle to interpret the information in forecasts, such as the meaning of below and above normal rainfall.…”

Section: Discussionmentioning

confidence: 80%

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Managing the water cycle in Kenyan small‐scale maize farming systems: Part 2. Farmers’ use of formal and informal climate forecasts

et al. 2015

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Forecasts can be an important component of water cycle management and farm decision making, particularly where rainfall is uncertain. In Kenya, the use of informal or indigenous forecasting (IF) is known to be widespread, but farmers also use more formal seasonal forecasting (SF) to make decisions in relation to the water cycle. A review of literature indicates that local knowledge is adaptable and often mixes indigenous knowledge and external information. In most cases, IF focusses on more local features and relates more to practical farm activities. Based on extensive interviews and focus groups of farmers in two study areas in Kenya, a majority of farmers from each area used IF, even though SF was also used. There were few farmer characteristics that explained the difference in the use of IF and SF. However, there were differences between the two study areas in how farmers interpreted SF and whether they used a combination of SF and IF. Furthermore there were differences in the local IF indicators used by farmers to identify the onset of the rainy season. To improve the acceptance of SF and increase its use, the information provided in SF needs to be easily incorporated into farmers’ current working knowledge, which might also include IF. This means that SF tools should be developed using participatory approaches to ensure the engagement of local farmers. WIREs Water 2016, 3:127–140. doi: 10.1002/wat2.1121 This article is categorized under: Human Water > Water as Imagined and Represented

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“…However, limited farm level flexibility due to capital or labor constraints was also listed as a reason for limited uptake of forecasts by farmers in Kenya. 40 In other words, while farmers can have access to forecasts, different factors can constrain their ability to react to the forecasts.…”

Section: Perceptions and Water Resourcesmentioning

confidence: 99%

Managing the water cycle in Kenyan small‐scale maize farming systems: Part 1. Farmer perceptions of drought and climate variability

et al. 2015

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Farmer perceptions clearly influence the adoption of technology and adaptation to climate change, but may not be consistent with or captured by scientific measurements. There has been a significant research on how perceptions influence water resource management and adaptation to climate, but conclusions are unclear or contradictory. This research aimed to clarify what shapes perceptions and how this understanding can refine meteorological data collection and to make more relevant and useful tools for farmers to adapt to changes in the water cycle. A survey of 244 small‐scale maize farmers was conducted using a questionnaire and semi‐structured interviews in two districts in southern and western Kenya which differed in climate type and farming systems. Farmer perceptions of and adaptation to climate uncertainty were investigated and compared with meteorological data. Most farmers perceived changes in the patterns of rainfall and dry spells, including later onset of rains than in the past. They have already adjusted their management based on these perceptions, including later planting times. Despite this, analysis of meteorological data indicated no major trends in rainfall or dry spell patterns in the two regions. This research confirms that the perception that the water cycle is changing is based on a combination of climatic, economic, or social observations, and farmers are already changing their management to adapt to the perceived changes in climate. The article explores the reasons why these perceptions were inconsistent with the available meteorological data and suggests that research may improve the usefulness of meteorological data to farmers. WIREs Water 2016, 3:105–125. doi: 10.1002/wat2.1118 This article is categorized under: Human Water > Water as Imagined and Represented

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Perception and Use of Climate Forecast Information Amongst Smallholder Farmers in Semi-Arid Kenya

Cited by 22 publications

References 13 publications

Simulating adaptation strategies to offset potential impacts of climate variability and change on maize yields in Embu County, Kenya

Simulating adaptation strategies to offset potential impacts of climate variability and change on maize yields in Embu County, Kenya

Managing the water cycle in Kenyan small‐scale maize farming systems: Part 2. Farmers’ use of formal and informal climate forecasts

Managing the water cycle in Kenyan small‐scale maize farming systems: Part 1. Farmer perceptions of drought and climate variability

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