Crop improvement is necessary for food security as the global population is expected to exceed 9 billion by 2050. Limitations in water resources and more frequent droughts and floods will make it increasingly difficult to manage agricultural resources and increase yields. Therefore, we must improve our ability to monitor agronomic research plots and use the information they provide to predict impacts of moisture stress on crop growth and yield. Towards this end, agronomists have used reductions in leaf expansion rates as a visible ‘plant-based’ indicator of moisture stress. Also, modeling researchers have developed crop models such as AquaCrop to enable quantification of the severity of moisture stress and its impacts on crop growth and yield. Finally, breeders are using Unmanned Aircraft Systems (UAS) in field-based High-Throughput Phenotyping (HTP) to quickly screen large numbers of small agronomic research plots for traits indicative of drought and flood tolerance. Here we investigate whether soybean canopy cover and color time series from high-resolution UAS ortho-images can be collected with enough spatial and temporal resolution to accurately quantify and differentiate agronomic research plots, pinpoint the timing of the onset of moisture stress, and constrain crop models such as AquaCrop to more accurately simulate the timing and severity of moisture stress as well as its impacts on crop growth and yield. We find that canopy cover time series derived from multilayer UAS image ortho-mosaics can reliably differentiate agronomic research plots and pinpoint the timing of reductions in soybean canopy expansion rates to within a couple of days. This information can be used to constrain the timing of the onset of moisture stress in AquaCrop resulting in a more realistic simulation of moisture stress and a lower likelihood of underestimating moisture stress and overestimating yield. These capabilities will help agronomists, crop modelers, and breeders more quickly develop varieties tolerant to moisture stress and achieve food security.

Potatoes (Solanum tuberosum) were grown in a fine sandy loam soil in southern Manitoba in a three-year field study comparing four water management treatments: No Drainage with No Irrigation (NDNI), No Drainage with Overhead Irrigation (NDIR), Free Drainage with Overhead Irrigation (FDIR), and Controlled Drainage with Subirrigation (CDSI). The objectives of the study were (i) to evaluate the effect of the four treatments on yield and quality of potatoes, (ii) to evaluate the effect of water management on the environment, (iii) to estimate the shallow groundwater contribution to potato water requirement, and (iv) to simulate the shallow groundwater hydrology using the DRAINMOD and HYDRUS 1-D model. Subsurface drains were installed at 0.9 m depth and at spacings of 15 m (FDIR) and 8 m (CDSI). Subirrigation was done by pumping water back into the tiles through the drainage control structures. Overhead irrigation was carried out using a travelling gun. Water table depth, soil water content, drainage outflow, nutrient concentration in drainage water, irrigation rate, weather variables, potato yield and quality parameters, and biomass were measured. Compared to the NDNI treatment, the potato yield increase in the other treatments ranged between 15-32% in 2011 and 2-14% in 2012. In 2011, potato yield from FDIR was higher than CDSI (p = 0.011) and NDNI (p = 0.001), and yield from NDIR was higher than NDNI (p = 0.034). In 2012, potato yield was higher in FDIR in comparison to NDNI (p = 0.021). In 2012, the NDIR gave higher dark ends (p = 0.008) compared to other treatments. Under dry conditions, up to 92% of the potato crop water demand could be met by shallow groundwater contribution. Compared to free drainage, controlled drainage was able to lower the nitrate export by 98% (p = 0.033) in 2010 and by 67% (p = 0.076) in 2011, and the phosphate export decreased by 94% (p = 0.0117) in 2010. A major part of the drainage flow and nutrient export took place between April and June in southern Manitoba. DRAINMOD was able to accurately predict the shallow groundwater hydrology for this particular research site.