Abstract
Citrus is considered one of the most important fruit crops globally due to its contribution to food and nutritional security. However, the production of citrus has recently been in decline due to many biological, environmental, and socio-economic constraints. Amongst the biological ones, pests and diseases play a major role in threatening citrus quantity and quality. The most damaging disease in Kenya, is the African citrus greening disease (ACGD) or Huanglongbing (HLB) which is transmitted by the African citrus triozid (ACT), Trioza erytreae. HLB in Kenya is reported to have had the greatest impact on citrus production in the highlands, causing yield losses of 25% to 100%. This study aimed at predicting the occurrence of ACT using an ecological habitat suitability modeling approach. Specifically, we tested the contribution of vegetation phenological variables derived from remotely-sensed (RS) data combined with bio-climatic and topographical variables (BCL) to accurately predict the distribution of ACT in citrus-growing areas in Kenya. A MaxEnt (maximum entropy) suitability modeling approach was used on ACT presence-only data. Forty-seven (47) ACT observations were collected while 23 BCL and 12 RS covariates were used as predictor variables in the MaxEnt modeling. The BCL variables were extracted from the WorldClim data set, while the RS variables were predicted from vegetation phenological time-series data (spanning the years 2014–2016) and annually-summed land surface temperature (LST) metrics (2014–2016). We developed two MaxEnt models; one including both the BCL and the RS variables (BCL-RS) and another with only the BCL variables. Further, we tested the relationship between ACT habitat suitability and the surrounding land use/land cover (LULC) proportions using a random forest regression model. The results showed that the combined BCL-RS model predicted the distribution and habitat suitability for ACT better than the BCL-only model. The overall accuracy for the BCL-RS model result was 92% (true skills statistic: TSS = 0.83), whereas the BCL-only model had an accuracy of 85% (TSS = 0.57). Also, the results revealed that the proportion of shrub cover surrounding citrus orchards positively influenced the suitability probability of the ACT. These results provide a resourceful tool for precise, timely, and site-specific implementation of ACGD control strategies.
Highlights
Citrus is considered one of the most important fruit crops in the world due to its contribution to food and nutritional security [1]
The bio-climatic and topographical variables (BCL) variables were extracted from the WorldClim data set, while the RS variables were predicted from vegetation phenological time-series data and annually-summed land surface temperature (LST) metrics (2014–2016)
A combined BCL and the RS variables (BCL-RS) model gave the highest accuracy of 92% with a true skill statistic (TSS) score of 0.83 compared to the model with only environmental variables (BCL model), which had an overall accuracy of 85% and a TSS score of 0.572
Summary
Citrus is considered one of the most important fruit crops in the world due to its contribution to food and nutritional security [1]. Citrus is the top-ranked fruit crop with regard to its international trade value [2]. Sweet oranges represent approximately 70% of citrus production. In 2016, the global total production of sweet oranges was about 73 million tons [3]. In Kenya, citrus is a valuable fruit crop used mainly for domestic consumption as a fresh produce with only a small quantity being processed into juice and jams [4]. In terms of the area of production, citrus (mainly oranges) ranks third (7268 ha) after bananas (63,299 ha) and mangoes (54,332 ha) in the country [5]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
