► Identifying genetic markers for yield requires rapid quantification of crop traits. ► Proximal sensing offers promise for field-based phenotyping (FBP). ► Efficient data integration and ...modeling-assisted analysis are key for FBP. ► FBP scaled to thousands of field plots is a feasible, attainable goal. ► FBP systems require new, integrative collaborations that cross disciplines.
A major challenge for crop research in the 21st century is how to predict crop performance as a function of genetic architecture. Advances in “next generation” DNA sequencing have greatly improved genotyping efficiency and reduced genotyping costs. Methods for characterizing plant traits (phenotypes), however, have much progressed more slowly over the past 30 years, and constraints in phenotyping capability limit our ability to dissect the genetics of quantitative traits, especially those related to harvestable yield and stress tolerance. As a case in point, mapping populations for major crops may consist of 20 or more families, each represented by as many as 200 lines, necessitating field trials with over 20,000 plots at a single location. Investing in the resources and labor needed to quantify even a few agronomic traits for linkage with genetic markers in such massive populations is currently impractical for most breeding programs. Herein, we define key criteria, experimental approaches, equipment and data analysis tools required for robust, high-throughput field-based phenotyping (FBP). The focus is on simultaneous proximal sensing for spectral reflectance, canopy temperature, and plant architecture where a vehicle carrying replicated sets of sensors records data on multiple plots, with the potential to record data throughout the crop life cycle. The potential to assess traits, such as adaptations to water deficits or acute heat stress, several times during a single diurnal cycle is especially valuable for quantifying stress recovery. Simulation modeling and related tools can help estimate physiological traits such as canopy conductance and rooting capacity. Many of the underlying techniques and requisite instruments are available and in use for precision crop management. Further innovations are required to better integrate the functions of multiple instruments and to ensure efficient, robust analysis of the large volumes of data that are anticipated. A complement to the core proximal sensing is high-throughput phenotyping of specific traits such as nutrient status, seed composition, and other biochemical characteristics, as well as underground root architecture. The ability to “ground truth” results with conventional measurements is also necessary. The development of new sensors and imaging systems undoubtedly will continue to improve our ability to phenotype very large experiments or breeding nurseries, with the core FBP abilities achievable through strong interdisciplinary efforts that assemble and adapt existing technologies in novel ways.
ABSTRACT
Increasing interest in deploying multiple types of sensing instruments for agricultural plot‐level observations has created a need for simple, high‐clearance vehicles that can be easily ...maneuvered through crops while minimizing damage due to wheel traffic. We describe a simple cart built from a 2‐m‐wide by 1.2‐m‐long steel frame that was welded onto two bicycle frames at a height providing 1 m of vertical clearance. Instruments such as radiometers and infrared thermometers are attached to the frame via arms that are secured with U‐bolts. A large, horizontal surface allows mounting data loggers, batteries, or computers. The cart is easily maneuvered by one person on level ground or by two persons on terrain with furrows, berms, or other obstacles. Design sketches and lists of materials are provided in an electronic supplement. The basic design is readily modifiable for different interrow spacings and sensor positions.
Active radiometric reflectance is useful to determine plant characteristics in field conditions. However, the physics of silicone diode-based sensing are temperature sensitive, where a change in ...temperature affects photoconductive resistance. High-throughput plant phenotyping (HTPP) is a modern approach using sensors often mounted to proximal based platforms for spatiotemporal measurements of field grown plants. Yet HTPP systems and their sensors are subject to the temperature extremes where plants are grown, and this may affect overall performance and accuracy. The purpose of this study was to characterize the only customizable proximal active reflectance sensor available for HTPP research, including a 10 °C increase in temperature during sensor warmup and in field conditions, and to suggest an operational use approach for researchers. Sensor performance was measured at 1.2 m using large titanium-dioxide white painted field normalization reference panels and the expected detector unity values as well as sensor body temperatures were recorded. The white panel reference measurements illustrated that individual filtered sensor detectors subjected to the same thermal change can behave differently. Across 361 observations of all filtered detectors before and after field collections where temperature changed by more than one degree, values changed an average of 0.24% per 1 °C. Recommendations based on years of sensor control data and plant field phenotyping agricultural research are provided to support ACS-470 researchers by using white panel normalization and sensor temperature stabilization.
There is a need for methodology to warm open-field plots in order to study the likely effects of global warming on ecosystems in the future. Herein, we describe the development of arrays of more ...powerful and efficient infrared heaters with ceramic heating elements. By tilting the heaters at 45° from horizontal and combining six of them in a hexagonal array, good uniformity of warming was achieved across 3-m-diameter plots. Moreover, there do not appear to be obstacles (other than financial) to scaling to larger plots. The efficiency ηh (%); thermal radiation out per electrical energy in of these heaters was higher than that of the heaters used in most previous infrared heater experiments and can be described by: ηh= 10 + 25exp(- 0.17 μ), where μ is wind speed at 2 m height (m s-1). Graphs are presented to estimate operating costs from degrees of warming, two types of plant canopy, and site windiness. Four such arrays were deployed over plots of grass at Haibei, Qinghai, China and another at Cheyenne, Wyoming, USA, along with corresponding reference plots with dummy heaters. Proportional integral derivative systems with infrared thermometers to sense canopy temperatures of the heated and reference plots were used to control the heater outputs. Over month-long periods at both sites, about 75% of canopy temperature observations were within 0.5 °C of the set-point temperature differences between heated and reference plots. Electrical power consumption per 3-m-diameter plot averaged 58 and 80 kW h day-1 for Haibei and Cheyenne, respectively. However, the desired temperature differences were set lower at Haibei (1.2 °C daytime, 1.7 °C night) than Cheyenne (1.5 °C daytime, 3.0 °C night), and Cheyenne is a windier site. Thus, we conclude that these hexagonal arrays of ceramic infrared heaters can be a successful temperature free-air-controlled enhancement (T-FACE) system for warming ecosystem field plots.
Plant height is a morphological characteristic of plant growth that is a useful indicator of plant stress resulting from water and nutrient deficit. While height is a relatively simple trait, it can ...be difficult to measure accurately, especially in crops with complex canopy architectures like cotton. This paper describes the deployment of four nadir view ultrasonic transducers (UTs), two light detection and ranging (LiDAR) systems, and an unmanned aerial system (UAS) with a digital color camera to characterize plant height in an upland cotton breeding trial. The comparison of the UTs with manual measurements demonstrated that the Honeywell and Pepperl+Fuchs sensors provided more precise estimates of plant height than the MaxSonar and db3 Pulsar sensors. Performance of the multi-angle view LiDAR and UAS technologies demonstrated that the UAS derived 3-D point clouds had stronger correlations (0.980) with the UTs than the proximal LiDAR sensors. As manual measurements require increased time and labor in large breeding trials and are prone to human error reducing repeatability, UT and UAS technologies are an efficient and effective means of characterizing cotton plant height.
The development of management plans which lead to water efficient landscapes is a growing need in the turfgrass community. While deficit irrigation as a scheduling method can improve water ...conservation, more information is desired on how to best leverage other management practices, such as mowing height when deficit irrigation is imposed. The objectives of this study were to characterize actual evapotranspiration (ETa), turfgrass visual quality, clipping production, and root development of ‘TifTuf’ bermudagrass (Cynodon dactylon × C. transvaalensis Burt Davy) when irrigated at full (1.0 × ETa) and deficit levels (0.65 and 0.30 × ETa), and cut at four separate mowing heights (2.5, 5.0, 7.5, and 10.0 cm) over two 8-week experimental runs. An elevated ETa was observed at the 7.5 cm and 10.0 cm mowing heights compared to the 2.5 cm mowing height in both runs, and the 5.0 cm mowing height in one run. The visual quality decreased throughout both study periods and mostly for the deficit irrigation treatments, with visual quality falling below minimum acceptable levels at the lowest irrigation level (0.30 × ETa) 5 weeks into run A, and 8 weeks into run B. Despite an elevated ETa and a higher root dry weight at higher mowing heights (7.5 and 10.0 cm), clipping production and visual quality was generally higher at lower mowing heights (2.5 and 5.0 cm) for both full and deficit irrigation levels. These results demonstrate that mowing height can significantly influence bermudagrass water use, as well as responses to deficit irrigation. When maintaining ‘TifTuf’ bermudagrass at heights above 2.5 cm, the results from this study indicate a lower water use and improved response to deficit irrigation at mowing heights ≤ 5 cm.
Remote-sensing using normalized difference vegetation index (NDVI) has the potential of rapidly detecting the effect of water stress on field crops. However, this detection has typically been ...accomplished only after the stress effect led to significant changes in crop green biomass, leaf area index, angle and position, and few studies have attempted to estimate the uncertainties of the regression models. These have limited the informed interpretation of NDVI data in agricultural applications. We built a ground-based sensing cart and used it to calibrate the relationships between NDVI and leaf water potential (LWP) for wheat, corn, and cotton growing under field conditions. Both the methods of ordinary least-squares (OLS) and weighted least-squares (WLS) were employed in data analysis, and measurement errors in both LWP and NDVI were considered. We also used statistical resampling to test the effect of measurement errors of LWP on the uncertainties of model coefficients. Our data showed that obtaining a high value of the coefficient of determination did not guarantee a high prediction precision in the obtained regression models. Large prediction uncertainties were estimated for all three crops, and the regressions obtained were not always significant. The best models were obtained for cotton with a prediction uncertainty of 27%. We found that considering measurement errors for both LWP and NDVI led to reduced uncertainties in model coefficients. Also, reducing the sample size of LWP measurement led to significantly increased uncertainties in the coefficients of the linear models describing the LWP-NDVI relationship. Finally, potential strategies for reducing the uncertainty relative to the range of NDVI measurement are discussed.
Leptin signaling pathways, stemming primarily from the hypothalamus, are necessary for maintaining normal energy homeostasis and body weight. In both rodents and humans, dysregulation of leptin ...signaling leads to morbid obesity and diabetes. Since leptin resistance is considered a primary factor underlying obesity, understanding the regulation of leptin signaling could lead to therapeutic tools and provide insights into the causality of obesity. While leptin actions in some hypothalamic regions such as the arcuate nuclei have been characterized, less is known about leptin activity in the hypothalamic ventromedial nuclei (VMN). Recently, pituitary adenylate cyclase-activating polypeptide (PACAP) has been shown to reduce feeding behavior and alter metabolism when administered into the VMN in a pattern similar to that of leptin. In the current study, we examined whether leptin and PACAP actions in the VMN share overlapping pathways in the regulation of energy balance. Interestingly, PACAP administration into the VMN increased STAT3 phosphorylation and SOCS3 mRNA expression, both of which are hallmarks of leptin receptor activation. In addition, BDNF mRNA expression in the VMN was increased by both leptin and PACAP administration. Moreover, antagonizing PACAP receptors fully reversed the behavioral and cellular effects of leptin injections into the VMN. Electrophysiological studies further illustrated that leptin-induced effects on VMN neurons were blocked by antagonizing PACAP receptors. We conclude that leptin dependency on PACAP signaling in the VMN suggests a potential common signaling cascade, allowing a tonically and systemically secreted neuropeptide to be more precisely regulated by central neuropeptides.
Field-based high-throughput plant phenotyping (FB-HTPP) has been a primary focus for crop improvement to meet the demands of a growing population in a changing environment. Over the years, breeders, ...geneticists, physiologists, and agronomists have been able to improve the understanding between complex dynamic traits and plant response to changing environmental conditions using FB-HTPP. However, the volume, velocity, and variety of data captured by FB-HTPP can be problematic, requiring large data stores, databases, and computationally intensive data processing pipelines. To be fully effective, FB-HTTP data workflows including applications for database implementation, data processing, and data interpretation must be developed and optimized. At the US Arid Land Agricultural Center in Maricopa Arizona, USA a data workflow was developed for a terrestrial FB-HTPP platform that utilized a custom Python application and a PostgreSQL database. The workflow developed for the HTPP platform enables users to capture and organize data and verify data quality before statistical analysis. The data from this platform and workflow were used to identify plant lodging and heat tolerance, enhancing genetic gain by improving selection accuracy in an upland cotton breeding program. An advantage of this platform and workflow was the increased amount of data collected throughout the season, while a main limitation was the start-up cost.
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