Detecting soil water redistribution in subsurface drip irrigated processing tomatoes using electrical resistivity tomography, proximal sensing and hydrological modelling.

In this study, multiple soil-plant-atmosphere continuum (SPAC) monitoring methodologies, including electrical resistivity tomography (ERT), proximal thermal sensing techniques, and micrometeorological data, were combined with two-dimensional (2-D) soil hydrological modelling using HYDRUS 2-D to explore the soil water redistribution, and infer the relative crop water status in a subsurface drip irrigated (SDI) processing tomato field located in California (Yolo County, USA). Specifically, time-lapse ERT surveys were performed at two transects distributed parallel and perpendicular, respectively, to the SDI line, during an irrigation event. The ERT results were compared to HYDRUS 2-D outputs and the relative differences were explained in the form of local heterogeneities in electrical resistivity (ER) changes, as a proxy for soil water content (SWC) variations. Concurrent simultaneous soil wetting and root water uptake during the last irrigation event of the season caused negligible changes in ER in the active root zone. Slight differences in ER were observed in the top 20 cm along the dripline, confirming that the emitter spacing is small enough to create a wetted strip along the processing tomato bed. These changes were also compared to SWC values measured with time domain reflectometry soil moisture sensors. A comparison between HYDRUS 2-D and ERT confirmed negligible changes in ER during irrigation due to simultaneous wetting and root water uptake processes. In addition, a good correlation was observed between the proximal sensed and the ERT results. Finally, the findings of this study underscore the necessity of using multiple methods for improving our knowledge of the SPAC system under real field conditions.

Uncertainty in climate change impact studies for irrigated maize cropping systems in southern Spain.

This study investigates the main drivers of uncertainties in simulated irrigated maize yield under historical conditions as well as scenarios of increased temperatures and altered irrigation water availability. Using APSIM, MONICA, and SIMPLACE crop models, we quantified the relative contributions of three irrigation water allocation strategies, three sowing dates, and three maize cultivars to the uncertainty in simulated yields. The water allocation strategies were derived from historical records of farmer's allocation patterns in drip-irrigation scheme of the Genil-Cabra region, Spain (2014-2017). By considering combinations of allocation strategies, the adjusted R2 values (showing the degree of agreement between simulated and observed yields) increased by 29% compared to unrealistic assumptions of considering only near optimal or deficit irrigation scheduling. The factor decomposition analysis based on historic climate showed that irrigation strategies was the main driver of uncertainty in simulated yields (66%). However, under temperature increase scenarios, the contribution of crop model and cultivar choice to uncertainty in simulated yields were as important as irrigation strategy. This was partially due to different model structure in processes related to the temperature responses. Our study calls for including information on irrigation strategies conducted by farmers to reduce the uncertainty in simulated yields at field scale.