Effect of nitrogen supply on stay-green sorghum in differing post-flowering water regimes.

MAIN CONCLUSION: The expression of stay-green (SG) characteristic in sorghum under water stress was related to N supply. SG genotype performed better than a non-stay-green (NSG) genotype at medium and high N levels. The differences in physiological parameters between SG and NSG genotypes were not significant at low N level and severe water stress. Grain sorghum [Sorghum bicolor (L.) Moench] with stay-green (SG) trait has the potential to produce more biomass and use soil water and nitrogen (N) more efficiently under post-flowering water stress. Previous studies were mostly conducted without N deficiency and more information is needed for interactions among soil N availability, SG genotype, and post-flowering water stress. In this study, the differences in leaf growth and senescence, shoot and root biomass, evapotranspiration (ET), water use efficiency (WUE), leaf photosynthetic responses, and nitrogen use efficiency (NUE) between a SG genotype (BTx642) and a non-stay-green (NSG) genotype (Tx7000) were examined. The two genotypes were grown at three N levels (Low, LN; Medium, MN; High, HN) and under three post-flowering water regimes (No water deficit, ND; Moderate water deficit, MD; Severe water deficit, SD). The genotypic difference was generally significant while it frequently interacted with N levels and water regimes. At medium and high N levels, SG genotype consistently had greater green leaf area, slower senescence rate, more shoot biomass and root biomass, and greater WUE and NUE than the NSG genotype under post-flowering drought. However, differences in several variables (e.g., leaf senescence, ET, WUE and NUE) between genotypes were not significant under SD at LN. At HN and MN, photosynthetic function of SG genotype was better maintained under drought. At LN, SG genotype maintained greater green leaf area but had lower photosynthetic activity than the NSG genotype. Nonetheless, adequate N supply is important for SG genotype under drought and greater root biomass may contribute to greater NUE in SG genotype.

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.

Host Suitability of Rapeseed for Heterodera schachtii.

Because rapeseed, especially canola, has the potential to be grown in rotation with sugarbeet in the north-central region of the United States, this study was initiated to assess its susceptibility to infection by Heterodera schachtii and to develop a screening method for Brassica germplasm. Existing methodology was adapted for growing Brassica juncea, B. napus, B. rapa, Brassica hybrids, and sugarbeet, Beta vulgaris, in H. schachtii-infested soil to count the females that developed on the roots. Cysts on sugarbeet contained a mean of 130 eggs compared with 240 for B. napus, lowest for the Brassica. Viability of eggs produced was assessed in soil planted with Brassica and sugarbeet and infested with with 0, 100, 1,000, 3,000, and 5,000 eggs to count resulting females and cysts. Number of females (y) was related linearly to infestation rate (x) by the regression equations y = 2.82 + 0.07(x) for the Brassica lines (R(2) = 0.79; P < 0.001) and y = 0.43 + 0.04(x) for sugarbeet (R(2) = 0.69; P < 0.007). These data indicated the potential for H. schachtii population increase if the two crops are used in rotation. All of the 111 germplasm lines tested were susceptible. The methodology developed during this research would benefit attempts to develop rapeseed cultivars resistant to H. schachtii.