Cold temperatures limit nitrate-N load reductions of woodchip bioreactors in higher-latitude climates. This two-year, on-farm (Willmar, Minnesota, USA) study was conducted to determine whether field-scale nitrate-N removal of woodchip bioreactors can be improved by the addition of cold-adapted, locally isolated bacterial denitrifying strains (bioaugmentation) or dosing with a carbon (C) source (biostimulation). In Spring 2017, biostimulation removed 66% of the nitrate-N load, compared to 21% and 18% for bioaugmentation and control, respectively. The biostimulation nitrate-N removal rate (NRR) was also significantly greater, 15.0 g N m-1 d-1, versus 5.8 and 4.4 g N m-1 d-1, for bioaugmentation and control, respectively. Bioclogging of the biostimulation beds limited dosing for the remainder of the experiment; NRR was greater for biostimulation in Fall 2017, but in Spring 2018 there were no differences among treatments. Carbon dosing did not increase outflow dissolved organic C concentration. The abundance of one of the inoculated strains, Cellulomonas sp. strain WB94, increased over time, while another, Microvirgula aerodenitrificans strain BE2.4, increased briefly, returning to background levels after 42 days. Eleven days after inoculation in Spring 2017, outflow nitrate-N concentrations of bioaugmentation were sporadically reduced compared to the control for two weeks but were insignificant over the study period. The study suggests that biostimulation and bioaugmentation are promising technologies to enhance nitrate removal during cold conditions. A means of controlling bioclogging is needed for biostimulation, and improved means of inoculation and maintaining abundance of introduced strains is needed for bioaugmentation. In conclusion, biostimulation showed greater potential than bioaugmentation for increasing nitrate removal in a woodchip bioreactor, whereas both methods need improvement before implementation at the field scale.
Tiny tomato pollen has an outsized role in reproduction, providing essential cellular and genetic material for fertilization and fruit generation. Unfortunately, high temperatures reduce pollination efficiency, harm fruit set and size, and ultimately diminish yield. This project attempts to answer basic questions about pollen growth and function during normal and heat-stressed conditions. Pollen from ~200 genetically diverse tomato and wild relative accessions will be observed as it grows at various temperatures. High-throughput microscopy will be paired with computer vision to phenotype the thousands of image sequences generated by this experiment. By combining pollen growth phenotypes with genome sequence data for all accessions, we plan to identify relevant genomic regions to target for functional description and crop improvement.
This paper proposes an interactive system called Andromeda1that enables users to interact with machine learning models to allow for exploratory sorting of images through a cognitive approach that uses a reduced dimension plot. In our system, a dimension reduction algorithm projects the images into a 2D space representing similarities between the images based on visual features extracted by a deep neural network. With Andromeda, users can alter the projection by dragging a subset of the images into groups according to their domain expertise. The underlying machine learning model learns the new projection by optimizing a weighted distance function in the feature space, and the model re-projects the images accordingly. The users can explore multiple custom projections to learn about the visual support for different groupings based on explainable-AI feedback. Our approach incorporates user preferences into machine learning model construction and allows transfer learning from pre-trained image processing models to accomplish new tasks based on user inputs. Using edamame pod images as an example, we interactively re-project the images into different groupings based on maturity and disease, and identify important visual features from the pixels highlighted by the model.
Phenotypic trait measurements have enabled breeders to link genomic information to phenotypic information and through this enhance crop performance by breeding for superior germplasm. Progress in this area has been hindered by the limited ability to capture agronomic traits of importance at a large field-based scale since traditional methods for measuring phenotypic traits in field are time and labor intensive and are limited in accuracy and consistency when implemented on large scale. Precision phenotyping efforts have enabled researchers at Corteva Agriscience to collect high quality datasets for important traits that programs had been unable to measure accurately or safely in the past. Specific applications of phenotyping technologies and how these have influenced data collection within Corteva breeding programs around the world will be presented. Technology is rapidly evolving, and by using this technology to develop novel precision phenotyping solutions, breeders are able to capture more high-quality data and gain unique insights.
A global, sun-blocking catastrophe like nuclear war, an asteroid strike, or super volcano eruption spells disaster for most aspects of life as we know it. There have been many studies on how differing magnitudes of sun-blocking catastrophes would affect the global climate, and many mention the effects of this cold, dark climate on forests and cropping systems. However, few studies have solely focused on the effects of nuclear winter on forests in terms of food, resources, and decomposition. Forests already provide over a billion people with food and fuel for their livelihoods. In this review we connect how prehistoric catastrophes affected the world's forests to how a current day catastrophe may affect forest health, forest resource availability, and wood decomposition rates. We briefly discuss how forest resources may be used in this post-catastrophe climate for food and fuel in an energy and fuel depleted world. We use this information to make policy and education suggestions to prepare for future catastrophes, build resilience from smaller local disasters, prepare for the many effects of climate change, and discourage nuclear weapon stockpiling.
Multi-plant imaging using arrays of low-cost cameras is a successful strategy for capturing affordable high-throughput plant phenotyping data. An imaging platform of this type can enable simultaneous imaging of hundreds to thousands of plants. The resulting datasets enable analysis of dynamic plant growth, development, and environmental responses at high temporal resolution. Full analysis of these datasets requires the identification of individual plants for measurement, but computational separation of individual plants becomes challenging when neighboring plants overlap. Here, we introduce the use of the watershed transform to segment moderately overlapping plants in multi-plant time series datasets. Rather than focusing on segmenting plants in individual images, we utilize information encoded in the entire time series to propagate plant labels from an early time point when individual plants are separate to later time points. In preliminary studies, using this method allowed us increase the analyzable size of the dataset by 28%.
Increased industrialization and pollution necessitate the development of new energy sources while minimizing the impact on the environment. Camelina sativa (L.) Crantz is being investigated as a potential source of novel biofuels, especially as a source of isoparaffin-rich jet fuel. However, currently, there is a paucity of phenotypic and genotypic data for C. sativa. Using a collection of 236 C. sativa genotypes from Eurasia, we assessed the phenotypic diversity of the collection using image data from the Bellwether Foundation Phenotyping Facility at the Donald Danforth Plant Science Center. Traits were measured using PlantCV. Based on phenotypic data, we found that most of the C. sativa accessions fell into 15 groups. There is strong phenotypic variance within each of these groups. Our study provides insight into defining potential C. sativa ideotypes.
Plant architecture is an important contributing factor for enhanced yield production and quality. The architecture traits are analyzed for crop health monitoring and genetic manipulation for generating high yielding varieties. Computer vision methods applied on 3D pointcloud allow more accurate extraction of architecture traits but consume more time and memory compared to 2D images. This study aims to design light weight 3D deep network for Cotton plant part segmentation and derive seven architectural traits of mainstem height, mainstem diameter, branch inclination angle, branch diameter, and number of branches, nodes, and cotton bolls. The pointcloud data is collected using FARO LiDAR scanner. The mainstem, branches and cotton bolls are manually annotated using Open3D. The preprocessing steps of denoising, normalization and down sampling are applied. 3D Deep network is designed to sample 1024, 512 and 256 points where neighborhood aggregation is performed at radius levels of 1cm, 5cm, and 30cm respectively. Features for remaining points are interpolated. The features from each radius level are concatenated and passed to multi-layer perceptron for pointwise classification. Results indicate that mean IoU and accuracy of 84% and 94% are achieved respectively. A 6.5 times speedup in inference time and 2.4 times reduction in memory consumption compared to Pointnet++ is gained. After applying postprocessing on part segments, an R square value of more than 0.8 and mean absolute percentage error of less than 11% are achieved on all derived architecture traits. The trait extraction results indicate potential utility of this process in plant physiology and breeding programs.
Plant roots exhibit distinct architectural organization and overall shape. Current concepts to quantify architectural variation assume a homogeneous phenotype for a given genotype. However, this assumption neglects the observable variation in root architecture for two reasons: (i) sampling strategies are designed to capture architectural variation only for the most common phenotype, and (ii) traits are often measured locally within a root system and ignore the architectural organization. Here, we introduce a new concept: the phenotypic spectrum of crop roots to quantify architectural variation as the number of architecture types for one genotype in a specific environment. We use the shape descriptor DS-curve to characterize the whole root system architecture. Using DS curves as a core, we developed a computing pipeline that combines Kmeans++ clustering, outlier filtering and the Fréchet distance as a similarity metric to classify types of root architectures. Subsequently, we applied this pipeline to analyze a field dataset including three common bean (Phaseolus vulgaris) genotypes DOR364 (n=797), L88_57 (n=1772), and SEQ7 (n=768) under non-limiting and water-stressed conditions in 2015 and 2016. We found DOR364 showed five different root architecture types across environments, while L88_57 and SEQ7 showed four. The total variation within classified root architecture types of DOR364, L88_57, and SEQ reduced by 58.59%, 50.19% and 53.01%, compared to the variation of the complete data sets. DOR364 had stable fractions of root architecture types across environments. In contrast, L88_57 and SEQ7 showed more variation in their fractions. There was no significant biomass difference among root architecture types for all studied genotypes within each environment. As such, we hypothesize that the phenotypic spectrum might buffer the impact of environmental stresses as an acclimatization strategy by changing the composition of root architecture types at the population level.
To assist plant scientists, geneticists, and growers to understand crop-environment interactions, plant phenotyping is a powerful tool for improving crop cultivars and developing decision support systems in farm management. Recent trends use LiDAR to capture three-dimensional (3D) information from plants to analyze traits vital to plant growth and development. However, current terrestrial-based 3D analysis methodologies are time and labor intensive and can be a bottleneck when large agricultural fields need to be analyzed. Robotic technologies can be used to accelerate the field-based measurements of relevant plant features and optimize the high-throughput phenotyping process. In this paper, we present a robotic system with a 3D LiDAR and a data processing pipeline for efficient, high-throughput field phenotyping of cotton crops. The robotic system consists of a Husky robotic platform equipped with a FARO Focus 3D laser scanner. The components of the system are integrated under the ROS framework to ensure interoperability and data integrity and availability at any given time. The data processing pipeline involves the data collection, registration, and analysis tasks for measuring crop traits at the plot level—canopy height, volume, and light interception—and estimating yield. This work demonstrates a crop phenotyping platform that leverages two off-the-shelf equipment for the quantitative assessment of cotton plant traits in the field. This methodology can be extended to other agricultural crops contributing to the advancement of plant phenomics.
Fire regimes are influenced by both exogenous drivers (e.g., increases in atmospheric CO2; and climate change) and endogenous drivers (e.g., vegetation and soil/litter moisture), which constrain fuel loads and fuel aridity. Herein, we identified how exogenous and endogenous drivers can interact to affect fuels and fire regimes in a semiarid watershed in the inland northwestern U.S. throughout the 21st century. We used a coupled ecohydrologic and fire regime model to examine how climate change and CO2 scenarios influence fire regimes over space and time. In this semiarid watershed we found that, in the mid-21st century (2040s), the CO2 fertilization effect on vegetation productivity outstripped the effects of climate change-induced fuel decreases, resulting in greater fuel loading and, thus, a net increase in fire size and burn probability; however, by the late-21st century (2070s), climatic warming dominated over CO2 fertilization, thus reducing fuel loading and fire activity. We also found that, under future climate change scenarios, fire regimes will shift progressively from being flammability to fuel-limited, and we identified a metric to quantify this shift: the ratio of the change in fuel loading to the change in its aridity. The threshold value for which this metric indicates a flammability versus fuel-limited regime differed between grasses and woody species but remained stationary over time. Our results suggest that identifying these thresholds in other systems requires narrowing uncertainty in exogenous drivers, such as future precipitation patterns and CO2 effects on vegetation.
Physiological dynamics at plant level are essential but also challenging for precision agriculture applications linked to plant phenotyping. In this study, we explore not only the spatial dynamics of corn in field conditions but also their temporal analysis via skeleton reconstruction of individual plants as a shape descriptor. For this purpose, an optimized approach for high-throughput was developed by point cloud data derived from UAS imagery. The curve-skeleton extraction is calculated based on a constrained Laplacian smoothing algorithm. The experimental setup was performed at the Indiana Corn and Soybean Innovation Center at the Agronomy Center for Research and Education (ACRE) in West Lafayette, Indiana, USA. On July 27th and August 3rd of 2021, two flights were performed over a trial with more than 200 maize plants using a custom designed UAS platform with a Sony Alpha ILCE-7R photogrammetric sensor. RGB images were processed by a standard photogrammetric pipeline by Structure from Motion (SfM) to get a scaled 3D point cloud of the individual corn. Filtering techniques and labeling algorithms were joined together to reconstruct a robust and accurate skeleton of individual maize. Therefore, significant traits such as number, length, growth angle and elongation rate of leaves and stem can be easily extracted. Height variations computed from the skeleton at the two dates show a coefficient of correlation with on-field measurements better than 92%. Our experimental outcomes demonstrate the UAS-data’s ability to provide practical information to efficiently select phenotypes in plant breeding programs.
Amending soils with sewage sludge biochar is a promising waste management strategy and value-added approach to reuse the waste while minimizing environmental contamination risks. Soil pot experiment was conducted to examine the effect of a 300°C sludge-biochar in soil health and crop productivity using a strongly acidic soil. Three treatments of the soil pots were included: 1% biochar– (10 g kg-1 biochar/soil ratio), 2% biochar– (20 g kg-1 biochar/soil ratio), and control (soil without biochar). Winter wheat (Triticum aestivum L.), spinach (Spinacia oleracea), and mung bean (Vigna radiata) were grown sequentially in the soil pots over 9 months under greenhouse and field conditions. Plant biomass and soil health parameters were assessed. Soils amended with 2% biochar demonstrated higher biomass in winter wheat, spinach, and mung bean compared to unamended control treatments. The effect of sludge biochar was not observed in soil bulk density; however, soil aggregates stability was higher in soils amended with 2% biochar (24.17%) compared to control (21.38%). Soil acidity was corrected in soils amended with 2% biochar (pH value 6.5) compared to control (5.8), electric conductivity (EC) was higher in 1% biochar (0.25 dS m-1) compared to control (0.20 dS m-1). Respiration rate was higher in 1% biochar (0.52 mg CO2 g-1 dry soil) compared to control (0.43 mg CO2 g-1), and total organic carbon (TOC) was lower in soils amended with biochar compared to control. Sewage sludge derived biochar improved crop production and soil health in strongly acidic soils and should be adopted in commercial agriculture.
Covid- 19 dominantly impacted the Indian agricultural sector. During the period of COVID-19 the southwest monsoon covered a major part of the country, thus resulting in an increase of 9 percent coverage in rainfall than the usual average period. Due to the good amount of rainfall the area under cultivation during the kharif season stood above 4.8% than the previous year. During, the initial lockdown period the agriculture has not been much affected and an increase in migration resulted an increase in people employed in agriculture. Through regression analysis the relationship between the yield and rainfall has been determined. The R2 values have been calculated and the spatial relationship between them has been established. Regions with higher R2 values have been found to be more dominantly affected by Covid-19, though in certain areas strong R2 has shown a weaker spatial relationship owing to certain other factors and policies taken by the Government. Therefore, regression analysis can be used as a suitable method to study the relationship of rainfall and agricultural yield during Covid-19. Keywords: Agriculture, Regression Analysis, Spatial relationship, Rainfall, Covid-19.
Precision yield data is commonly recorded by modern combine harvesters and can be used to help growers optimize their operations. However, there have been very few attempts to predict variation in yield within a given field using multispectral satellite data. We used a precision yield dataset gathered in canola (Brassica napus L.) crops in central Alberta, Canada, and a time series of medium-resolution Sentinel-2 data collected over the growing season. Using two mapping methods, random forest regression and functional data analysis, we were able to predict crop yield to within 12-16% accuracy of actual yield, and to capture within-field variation. Our results demonstrate that time series of medium-resolution multispectral imagery is capable of mapping small-scale variation in crop yields, presenting new research and management applications for these techniques.
Yield forecasting can give early warning of food risks and provide theoretical support for food security planning. Climate change and land use change directly influence the regional yield and planting area of maize, but few existing studies have examined their synergistic impact on maize production. In this study, we combine system dynamic (SD), the future land use simulation (FLUS) and a statistical crop model to predict future maize yield variation in response to climate change and land use change. Specifically, SD predicts the future land use demand, FLUS simulates future spatial land use patterns, and a statistical maize yield model based on regression analysis is utilized to adjust the per hectare maize yield under four representative concentration pathways (RCPs). A phaeozem region in central Jilin Province of China is taken as a case study. The results show that the future land use pattern will significantly change from 2030 to 2050. Although the cultivated land is likely to reduce by 862.84 km2, the total maize yield in 2050 will increase under all four RCP scenarios due to the promotion of per hectare maize yield. RCP4.5 will be more beneficial to maize production than other scenarios, with a doubled total yield in 2050. Notably, the yield gap between different counties will be further widened, which necessitates the differentiated policies of agricultural production and farmland protection, e.g., strengthening cultivated land protection and crop management in low-yield areas, as well as taking adaptation and mitigation measures to coordinate climate change and crop production.
Teff (Eragrostis tef) is an underutilized cereal grown primarily by small-scale farmers in Ethiopia, where it thrives under arid conditions unsuitable for other grain crops. Incomplete selection of classic domestication traits such as lodging, panicle architecture, and seed density contribute to the low yields observed in teff compared to leading cereals. To investigate the phenotypic basis of lodging tolerance in teff, we surveyed domestication related traits across a diversity panel of 265 teff wild relatives, landraces, and cultivars in Michigan. Panicle architecture and lodging score were collected in the field. To strengthen ground truth data and identify spectral signatures of plant height and subsequent lodging, LIDAR and hyperspectral images were collected with an unmanned aerial vehicle. A tiller imaging box was designed to maintain plant architecture from the field in a controlled lab environment. Morphological features including panicle height, panicle width, spikelet density, panicle angle, and tiller angle will be calculated using PlantCV and ImageJ. Feature evaluation via Pearson’s correlation and analysis of variance will be conducted for structural and morphological traits. This data will be used in a genome wide association study to identify phenotypes underlying lodging tolerance, and superior breeding material will be isolated for future studies.
In the United States, voluntary and compliance carbon markets are being created there is an effort to match producers of carbon credits with those that would like to purchase credits. Each market has unique obligations and requirements. Farmers and those advising farmers are confused about the marketplace. The purpose of this document is to provide useful information about the voluntary and compliance markets. The target audience is farmers, crop consultants, and scientists. The document is organized into four sections, general information about the markets, answers to questions from certified crop consultants, market glossary, and requirements about specific markets. These marketplaces are rapidly evolving and will likely change as the markets mature.