Climate change impacts on date palm cultivation in Saudi Arabia

Date palm (Phoenix dactylifera L.) is an important cash crop in many countries, including Saudi Arabia. Understanding the likely potential distribution of this crop under current and future climate scenarios will enable environmental managers to prepare appropriate strategies to manage the changes. In the current study, the simulation model CLIMEX was used to develop a niche model to estimate the impacts of climate change on the current and future potential distribution of date palm. Two global climate models (GCMs), CSIRO-Mk3.0 and MIROC-H under the A2 emission scenario for 2050 and 2100, were used to assess the impacts of climate change. A sensitivity analysis was conducted to identify which model parameters had the most effect on date palm distribution. Further refinements of the potential distributions were performed through the integration of six non-climatic parameters in a geographic information system. Areas containing suitable soil taxonomy, soil texture, soil salinity, land use, landform and slopes o

Evaluation of integrative hierarchical stepwise sampling for digital soil mapping

This paper presents an integrative hierarchical stepwise sampling (IHS) method and two case studies to compare it with stratified random sampling (SRS) and conditioned Latin hypercube sampling (cLHS). The first comparison between IHS and SRS was conducted for mapping sand content of two soil layers in a study area in Anhui Province, China. Two sample sets of the same sample size were collected in the field based on IHS and SRS. The second case study is a simulation study, where we compared IHS and cLHS for mapping soil series in the Raffelson watershed in Wisconsin (USA). The study used an accurate and detailed soil series map produced previously as a proxy of the real soil distribution. Virtual samples with nine sample sizes designed by IHS and cLHS were collected on the soil map. For both case studies, an individual predictive soil mapping method was employed and independent validation samples were used to evaluate the mapping accuracies. Results indicate that IHS generally performs better than SRS for capturing distributions of the environmental variables. It obtained higher mapping accuracies than SRS at different sample sizes. On the other hand, cLHS appears to provide a better representation for distributions of the environmental variables than IHS, but the mapping accuracies with IHS are higher than those with cLHS at nearly all sample sizes. Finally, both case studies showed that IHS provides valuable information on representativeness of the samples