Estimation of Spatiotemporal Soil Moisture Dynamics in a Temperate Organic Alley Cropping System in Hessen, Germany

The exponentially growing population drives the intensification of agricultural production, which contributes to land and water quality degradation, biodiversity loss, and climate change. In this regard, nature-based solutions like silvoarable agroforestry systems, which integrate trees on arable land, have taken a new dawn due to their potential multifaceted benefits derived from nature’s contributions to people. Among the limiting factors in sustainable agricultural production is water availability, which governs biogeochemical processes, such as the regulation of material fluxes, nutrient availability and movement, carbon sequestration, microbial activity, and modification of soil properties. In temperate agroforestry systems, soil moisture regimes are not well understood. Efforts in collecting long-term data are of high importance, particularly in determining how agroforestry systems in temperate climates affect water availability and, therefore, their potential to support food production under current and future climate conditions. Knowledge of soil moisture could help in understanding whether agroforestry systems improve water availability for crop growth, which would offer resilience against droughts, or, on the other hand, cause competition with trees that reduces soil moisture availability.

In this ongoing study, we investigate point- and field-scale soil moisture dynamics in a six-year-old organic alley cropping system in Hessen, Germany. The system consists of six strips of 3-meter-wide tree rows with apple, poplar, and timber trees, alternated with 18-meter-wide crop alleys. We instrumented three transects with Frequency Domain Reflectometry (FDR) soil moisture sensors at 1, 2.5, 6, and 10.5 meters perpendicular from the tree row (upslope and downslope) at 10, 40, and 60 cm depths, to study soil moisture dynamics along the tree-crop interface. We also employed three cosmic ray neutron sensors (CRNS) to assess the field-scale trend and dynamics of the soil moisture based on the inverse relationship of the amount of hydrogen (water) in the soil and the intensity of epithermal neutrons over its dynamic footprint. Here, we present our experimental setup to capture both the transect-point scale and field-scale spatiotemporal soil moisture patterns and show preliminary findings for a full cropping season. Such an approach has the potential to provide soil moisture data at different scales relevant to efficient system design, tree-crop species selection, and agricultural water management.