Assessing soil health in a thermic region of the southern great plains, using the soil management assessment framework (SMAF)

The soil management assessment framework (SMAF) has been widely used as a tool to quantify soil health. However, SMAF was developed using data from only a few climate conditions in the United States and regional verification is often suggested. We evaluated SMAF's short-term performance in a thermic/hyperthermic region, aiming to 1) evaluate the sensitivity of SMAF scores to changes in individual soil properties and 2) quantify soil health changes using SMAF. Treatments include two levels of summer crop and two levels of tillage in an annually planted wheat system. SMAF soil health metrics were measured for Burleson clay soil (BC site) and Parrita sandy clay loam soil (PSCL site) for 0–5 cm soil depth. At the BC site, βgluc and SOC SMAF scores displayed no statistical differences when compared to their respective soil properties. βgluc measurement also helped to highlight the treatment difference observed for wheat yield. This suggests that scoring curves of βgluc used in SMAF may need to be modified, especially for clayey soils. The results also show that SMAF scores were not correlated with wheat yield at both sites, suggesting that multiple year data may be needed to understand this relationship. Overall, in a thermic region, SMAF was found to be helpful to understand short-term soil health status. However, due to clay correction in SMAF algorithm, SMAF scores can show lower sensitivity in the clayey soils of these thermic regions

Effect of Air- and Water-Filled Voids on Neutron Moisture Meter Measurements of Clay Soil

Air- and water-filled voids around neutron moisture meter (NMM) access tubes have been cited as sources of volumetric water content (θ) 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 (ρ) 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 ρ and θ were measured. The RMSE of the NMM calibration using all 11 installations was 0.02 m m. However, if two cores were used for calibration, the ratio of NMM-measured θ to in situ θ was significantly different ( < 0.05) from measured θ half the time (RMSE, 0.012–0.05 m m). Small air-filled voids created drier estimates of θ (bias, −0.039 m m; < 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 ( < 0.001) than core-measured θ, with biases of −0.068 and 0.080 m m, respectively. Although this work does not correct NMM-predicted θ to matrix θ, it does bound NMM error under field conditions in a cracking clay soil

Case_Study_3_soil_plant_interaction_NIR

Raw NIR images with gps log and ground truth data for case study 3 soil and plants interaction. The RGB and NIR images were split as two files due to the file size limit

Case_Study_3_soil_plant_interaction_RGB

Raw RGB images with gps log and ground truth data for case study 3 soil and plants interaction. The RGB and NIR images were split as two files due to the file size limit

Senecio nikoensis Miq.

原著和名: サハギク科名: キク科 = Compositae採集地: 東京都 高尾山 (武蔵 高尾山)採集日: 1986/7/15採集者: 萩庭丈壽整理番号: JH007357国立科学博物館整理番号: TNS-VS-95735

Data from: Unmanned aerial vehicles for high-throughput phenotyping and agronomic research

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

Case_Study_1_plant_height_corn_Raw_Image_Data_3_of_10

Case_Study_1_plant_height_corn_Raw_Image_Data_3_of_1

NDVI map generated from multispectral data collected with the Sentek sensor onboard the Anaconda fixed wing UAV platform.

<p>NDVI map generated from multispectral data collected with the Sentek sensor onboard the Anaconda fixed wing UAV platform.</p

Comparison of different types of UAV platforms.

<p>Comparison of different types of UAV platforms.</p