English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Tea is a major cash crop in Kenya. Predicting the potential effects of climate change on tea crops prompts the use of statistical models to measure how the crop responds to climate variables. The statistical model was trained on historical tea yields, and how they related to past data on maximum temperature, minimum temperature and precipitation over Nandi East Sub-County. Scatter diagrams for selected months were generated from tea yield and temperature data. A multiple linear model was developed to predict tea yield using climatic variables. A contingency table was used to verify the model. Results from an analysis of trends in rainfall depicted a positive trend and revealed an increased frequency of annual droughts. The study showed that the frequency of extreme rainfall events during September-October-November (SON) season has decreased. Results from an analysis of the trends in temperature revealed that the minimum temperatures are increasing and that the frequency of extreme events has increased. Rising maximum temperatures were observed in March. The study revealed that May, the cold month, is becoming warmer. Correlation analysis indicated that the climatic variables during some months in both the concurrent year and the previous year were positively correlated with the tea yield. However, there was an inverse relationship between maximum temperature and rainfall. Results of model verification revealed that that 70% of model forecasts were correct. The results also showed that at least half of the observed events were correctly forecasted and thus the majority of the forecasts were true. An equation for predicting the yield of tea from the climate variables is presented.

On the Use of Regression Models to Predict Tea Crop Yield Responses to Climate Change: A Case of Nandi East, Sub-County of Nandi County, Kenya