Optimizing drip irrigation for eggplant crops in semi-arid zones using evolving thresholds

AbstractField experiments were combined with a numerical model to optimize drip irrigation management based on soil matric potential (SMP) measurements. An experimental crop of eggplant was grown in Burkina Faso from December 2014 to March 2015 and plant response to water stress was investigated by applying four different irrigation treatments. Treatments consisted in using two different irrigation depths (low or high), combined with a water provision of 150%, 100% or 66% (150/100/66) of the maximum crop evapotranspiration (T150low, T66low, T100high, T66high). Soil matric potential measurements at 5, 10 and 15cm depth were taken using a wireless sensor network and were compared with measurements of plant and root biomass and crop yields. Field data were used to calibrate a numerical model to simulate triggered drip irrigation. Different simulations were built using the software HYDRUS 2D/3D to analyze the impact of the irrigation depth and frequency, the irrigation threshold and the soil texture on plant transpiration and water losses. Numerical results highlighted the great impact of the root distribution on the soil water dynamics and the importance of the sensor location to define thresholds. A fixed optimal sensor depth of 10 cm was found to manage irrigation from the vegetative state to the end of fruit development. Thresholds were defined to minimize water losses while allowing a sufficient soil water availability for optimal crop production. A threshold at 10cm depth of −15kPa is recommended for the early growth stage and −40kPa during the fruit formation and maturation phase. Simulations showed that those thresholds resulted in optimal transpiration regardless of the soil texture so that this management system can constitute the basis of an irrigation schedule for eggplant crops and possibly other vegetable crops in semi-arid regions

Low-cost wireless sensor networks for dryland irrigation agriculture in Burkina Faso

Dryland irrigation is a major concern in arid and semiarid regions where agricultural output is low and water a scarce and vital resource. Irrigation efficiency and sustainability are, therefore, of paramount importance in these regions, where small farmers generally over-irrigate vegetables to avoid yield loss, resulting in excessive water consumption, unnecessary water pumping costs, and soil degradation. Improving dryland irrigation support requires field data, which is often scarce and unreliable in developing countries, being mostly collected manually with obsolete equipment. Modern automatic weather stations are costly, and local resources for station repair and maintenance are limited. The research project Info4Dourou2.0 aims to improve environmental data collection in developing countries by using low-cost wireless sensors networks (WSN). Hydrometeorological stations have been designed specifically for harsh environmental conditions and the limited local resources. They are simple to install and require little maintenance. The collected data is available in real time via a mobile phone and a web interface. These completely automatic stations have been developed by Ecole Polytechnique Fédérale de Lausanne (EPFL) and the start-up sensorscope, with the aim of being manufactured, assembled, maintained, and commercialized locally. Results of the present study show that by coupling autonomous and continuous measurements of meteorological variables with soil-water-plant-atmosphere models, we have designed a simple irrigation management system that has a strong potential to improve agricultural production: up to a 38 % yield increase has been achieved using 20 % less water compared to the unassisted way of irrigating