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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Visible and near‐infrared (VNIR, 400–2500 nm) diffuse reflectance spectroscopy (DRS) is a rapid, proximal‐sensing method that has proven useful in quantifying constituents of dried and ground soil samples. Very little is known, however, about how DRS performs in a field setting on soils scanned in situ. The overall goal of this research was to evaluate the feasibility of VNIR‐DRS for in situ quantification of clay content of soil from a variety of parent materials. Seventy‐two soil cores were obtained from six fields in Erath and Comanche counties, Texas. Each soil core was scanned with a visible near‐infrared spectrometer, with a spectral range of 350 to 2500 nm, at four different combinations of moisture content and pretreatment: field‐moist in situ, air‐dried in situ, field‐moist smeared in situ, and air‐dried ground. The VNIR spectra were used to predict total and fine clay content of the soil using partial least squares (PLS) regression. The PLS model was validated with 30% of the original soil cores that were randomly selected and not used in the calibration model. The validation clay predictions had a root mean squared deviation (RMSD) of 61 and 41 g kg−1 dry soil for the field‐moist and air‐dried in situ cores, respectively. The RMSD of the air‐dry ground samples was between the two in situ RMSDs and comparable to values in the literature. Smearing the samples increased the field‐moist in situ RMSD to 74 g kg−1 Whole‐field holdout validation results showed that soils from all parent materials need to be represented in the calibration samples for maximum predictability. In summary, DRS is an acceptable technique for rapidly measuring soil clay content in situ for various water contents and parent materials.

Soil degradation is a critical and growing global problem. As the world population increases, pressure on soil also increases and the natural capital of soil faces continuing decline. International policy makers have recognized this and a range of initiatives to address it have emerged over recent years. However, a gap remains between what the science tells us about soil and its role in underpinning ecological and human sustainable development, and existing policy instruments for sustainable development. Functioning soil is necessary for ecosystem service delivery, climate change abatement, food and fiber production and fresh water storage. Yet key policy instruments and initiatives for sustainable development have under‐recognized the role of soil in addressing major challenges including food and water security, biodiversity loss, climate change and energy sustainability. Soil science has not been sufficiently translated to policy for sustainable development. Two underlying reasons for this are explored and the new concept of soil security is proposed to bridge the science–policy divide. Soil security is explored as a conceptual framework that could be used as the basis for a soil policy framework with soil carbon as an exemplar indicator.