Advances in automation and data science have led agriculturists to seek real-time, high-quality, high-volume crop data to accelerate crop improvement through breeding and to optimize agronomic practices. Breeders have recently gained massive data-collection capability in genome sequencing of plants. Faster phenotypic trait data collection and analysis relative to genetic data leads to faster and better selections in crop improvement. Furthermore, faster and higher-resolution crop data collection leads to greater capability for scientists and growers to improve precision-agriculture practices on increasingly larger farms; e.g., site-specific application of water and nutrients. Unmanned aerial vehicles (UAVs) have recently gained traction as agricultural data collection systems. Using UAVs for agricultural remote sensing is an innovative technology that differs from traditional remote sensing in more ways than strictly higher-resolution images; it provides many new and unique possibilities, as well as new and unique challenges. Herein we report on processes and lessons learned from year 1—the summer 2015 and winter 2016 growing seasons–of a large multidisciplinary project evaluating UAV images across a range of breeding and agronomic research trials on a large research farm. Included are team and project planning, UAV and sensor selection and integration, and data collection and analysis workflow. The study involved many crops and both breeding plots and agronomic fields. The project’s goal was to develop methods for UAVs to collect high-quality, high-volume crop data with fast turnaround time to field scientists. The project included five teams: Administration, Flight Operations, Sensors, Data Management, and Field Research. Four case studies involving multiple crops in breeding and agronomic applications add practical descriptive detail. Lessons learned include critical information on sensors, air vehicles, and configuration parameters for both. As the first and most comprehensive project of its kind to date, these lessons are particularly salient to researchers embarking on agricultural research with UAVs.

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Though much has been done to understand proximally-sensed bulk apparent electrical conductivity (EC a) in agricultural soils, many of the soil properties identified to be mappable using these techniques, such as salinity and clay content, are not expected to drive EC a response in a non-saline Vertisol. In Vertisols, agrillipedoturbation creates meter-scale variability in soil moisture and chemical properties associated with gilgai features, and if developed from calcareous parent material, can exhibit meter and landscape scale variability in inorganic C content. The ability to map inorganic C may be especially useful in a Vertisol due to its strong correlation with shrink-swell potential. The overall goal of this project was to investigate the potential for mapping inorganic C using EC a surveys in a calcareous Vertisol, with the future goal of mapping shrink-swell potential on these landscapes. On a 40-by 50-m field with intact circular gilgai, EC a was mapped under both moist and dry soil conditions. Soil samples were taken for water content, clay content, inorganic C content, salinity, and depth to parent material. Under moist soil conditions, the strongest correlation to EC a was inorganic C content (r =-0.63), followed by water content (r = 0.49); however, under dry conditions, only inorganic C content was significant (r =-0.60). In addition, EC a surveys and inorganic C samples were taken for two larger watersheds of 10 and 14 ha. Again, inorganic C content was significantly and reliably correlated to EC a for both fields, and the resulting regression slopes and intercepts were not significantly different between watersheds, though the surveys were conducted at different times. Results suggest that EC a can be used to map inorganic C in Vertisols weathered from calcareous parent materials, allowing for spatial inference of shrink-swell potential which may be useful in distributed hydrology modeling.

Core Ideas Desiccation cracks make runoff and infiltration predictions difficult in expansive soils. A crack‐volume model based on water content and soil properties was developed. Crack volume was measured in situ using digital photography of excavated soil layers. The new model improved crack volume estimates over a model based on layer thickness. A critical gap in hydrology knowledge is predicting the partition of runoff and infiltration during rainfall events in shrink‐swell soils with desiccation cracks. Knowledge of surface cracking and crack volume is needed, but field measurements of these vertical soil cracks are time and labor intensive, and the results cannot be easily translated to another location. Our approach to predict soil crack volume at the pedon‐scale uses an existing soil shrinkage model, which has been modified to include soil water content and the coefficient of linear extensibility (COLE). To validate the model, measurements of soil layer thickness, water content, and crack volume were made for seven soils with COLE values from 0.01 to 0.17 m m–1. Soil crack volume was estimated by filling cracks with a cement slurry and photographing excavated soil layers at the end of the study. Over two drying and wetting cycles, the relationship between soil layer thickness and water content was linear. The modified crack volume equation, using COLE and water content, was a better fit to cement‐estimated crack volume, r2 from 0.06 to 0.61, than the existing shrinkage model. However, crack volume estimates by both models were six‐times higher than the cement‐estimated crack volume. It is possible the models of crack volume are including all changes to soil porosity from mega‐cracks to mesopores, while direct techniques only measure the larger‐scale cracks. The new, modified crack volume equation is a pedon‐validated equation that advances predictions of cracking extent in landscapes where shrink‐swell potential is variable in space.

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Air-and water-filled voids around neutron moisture meter (NMM) access tubes have been cited as sources of volumetric water content (q v ) measurement error in cracking clay soils. The objectives of this study were to experimentally quantify this potential error stemming from (i) uncertainty in bulk density (r b ) sampling and (ii) the impact of air-and water-filled voids. Air-and water-filled voids were simulated using ~0.6-cm (small) and ~1.9-cm (large) annuli around access tubes. After NMM measurements were taken in a tightly installed access tube, either a small or large annulus was installed in the same borehole. Additional NMM measurements were taken with the annulus filled with air, and then water and r b and q v were measured. The RMSE of the NMM calibration using all 11 installations was 0.02 m 3 m −3 . However, if two cores were used for calibration, the ratio of NMM-measured q v to in situ q v was significantly different (p < 0.05) from measured q v half the time (RMSE, 0.012-0.05 m 3 m −3 ). Small air-filled voids created drier estimates of q v (bias, −0.039 m 3 m −3 ; p < 0.001), wherease small water-filled voids were not significantly different from the calibration. Air-and water-filled voids from larger annuli were significantly lower and higher (p < 0.001) than core-measured q v , with biases of −0.068 and 0.080 m 3 m −3 , respectively. Although this work does not correct NMMpredicted q v to matrix q v , it does bound NMM error under field conditions in a cracking clay soil.Abbreviations: NMM, neutron moisture meter. Ackerson. 2018. Effect of air-and water-filled voids on neutron moisture meter measurements of clay soil. Vadose Zone J. 17:180137.

Tissue nitrate (NO3) concentration (TNC) in leafy greens generally decreases with increasing light intensity and photoperiod in controlled environment studies. Harvesting late in the day has been recommended as a way to produce leafy greens with lower TNC, although data from field research do not support this recommendation. This study investigated the effect of time of day of harvest on TNC in lettuce (Lactuca sativa L.) and spinach (Spinacia oleracea L.) grown in the field during the summer at Pullman, WA (lat. 46° N) and Fairbanks, AK (lat. 64° N). Whole plants were sampled every 2 h on three separate, 24-h harvest dates at each latitude. Plants were dried, ground, and analyzed for NO3-N. At the high-latitude location, TNC decreased linearly during the day (1000 to 2300 hr) on all three dates for spinach and one for lettuce. At the low-latitude location, TNC decreased linearly during the day (1000 to 1900 hr) on one date and increased linearly during the night (2000 to 0400 hr) on two dates for lettuce. The TNC (average 287 to 607 mg NO3-N/kg fresh weight for lettuce and 141 to 189 mg NO3-N/kg fresh weight for spinach) and magnitude of diurnal fluctuation (generally less than 25%) should not pose a human health risk regardless of when plants are harvested.