Whole plant chlorophyll fluorescence imaging is a powerful tool for non-destructive analysis of photosynthesis. Analysis of such images requires software that is able to process and calculate photosynthetic parameters per plant pixel. PlantCV is an open-source, Python-based library of image analysis tools for plant science. Previous versions of PlantCV included tools to analyze photosynthetic efficiency data, but recent developments to the photosynthesis subpackage have expanded to include more photosynthetic parameters based on chlorophyll fluorescence and spectral indices. This paper highlights the newest updates to the photosynthesis package of PlantCV and discusses applications of these tools on a sorghum dataset that was imaged with a PhenoVation CropReporter system.
In Canada, winter wheat must survive air temperatures as low as -30oC. Snow cover acts as a thermal blanket reducing the direct exposure to chilling air temperatures. In a joint Can/UK wheat project we measured vegetative cover (VC) on 88 different winter wheat varieties in the fall up to snow cover and directly after the snow melted in the spring. Comparing the VC pre- and post-winter provided a direct measure of winter hardiness. While the majority of the wheat cultivars from the UK experienced greater winter kill than the Canadian lines, there were still some with comparable hardiness. Fall and spring growth rates were determined and the UK lines had similar or greater fall growth rates than Canadian lines but lower spring growth rates. Phenomic determined hardiness was correlated to visual ratings and harvest yield.
Drought is a major threat to global agriculture and can trigger or intensify food price increase and migration. Assessment and monitoring are essential for proper drought management. Drought indices play a fundamental task in this respect. This research introduces the Wet-environment Evapotranspiration and Precipitation Standardized Index (WEPSI) for drought assessment and monitoring. WEPSI is inspired by the Standardized Precipitation Evapotranspiration Index (SPEI), in which water supply and demand are incorporated into the drought index calculation. WEPSI considers precipitation (P) for water supply and wet-environment evapotranspiration (ETw) for water demand. We use an asymmetric complementary relationship to calculate ETw using actual (ETa) and potential evapotranspiration (ETp). WEPSI is tested in the transboundary Lempa River basin located in the Central American dry corridor. ETw is estimated based on evapotranspiration data calculated using the Water Evaluation And Planning (WEAP) system hydrological model. To investigate the performance of our introduced drought index, we compare it with two well-known meteorological indices (Standardized Precipitation Index and SPEI), together with a hydrological index (Standardized Runoff Index), in terms of correlation and mutual information (MI). We also compare drought calculated with WEPSI and historical information, including crop cereal production and Oceanic Niño Index (ONI) data. The results show that WEPSI has the highest correlation and MI compared with the three other indices used. It is also consistent with the records of crop cereal production and ONI. These findings show that WEPSI can be applied for agricultural drought assessments.
Agricultural water resources are threatened by climatic variability and increased competition for available freshwater resources. In order to mitigate the effect of climate change on cotton production, breeders are increasing their efforts on improving drought tolerance in this essential fiber crop. To achieve this, effective screening of diverse germplasm is needed to identify useful genetic variation that can be utilized for crop improvement. Within the last decade, unmanned aerial vehicles (UAVs) have led to the ability to quickly and reliably image large areas while simultaneously decreasing temporal effects associated with a large time window for data collection. This technology allows researchers to scale their phenotyping efforts, enabling studies that utilize mapping and monitoring efforts such as plant water stress detection. In this study, we used UAV-based thermal imagery to screen a diverse population of over 350 different genotypes of cotton in order to locate varieties that exhibit cooler canopies. This diversity panel was grown under two contrasting levels of irrigation, well-watered and water-limited, with data collection flights occurring weekly for three months during the season. The thermal images were clipped to plot boundaries, soil and plant pixels were segmented, and average temperatures were extracted to identify potential drought tolerant varieties. The objectives of this study were to (i) demonstrate that UAV-based thermal imagery, along with our calibration methods, can be used to render accurate plant canopy temperature values and (ii) identify cotton genotypes that outperform others in a drought-stressed environment.
Stomata, microscopic pores on leaf surfaces, regulate the uptake of carbon dioxide and the simultaneous loss of water vapor by leaves. New image acquisition and analysis methods are allowing high-throughput phenotyping of stomatal patterning, which in turn have been applied to better understand the genetic basis of variation in certain species. However, it takes considerable data and effort to train the models, and their ability to accurately detect epidermal structures is constrained to morphologies found within the training data. This issue of context dependency, the inability to perform effectively in novel contexts, is the main hurdle preventing widespread adoption of machine learning in high-throughput phenotyping of intraspecific, interspecific, and environmental variation. Here we show the limited ability of a Mask-RCNN tool, which was previously trained and successfully applied to Zea mays, to analyze images from a closely related grass, Setaria viridis. We then demonstrate successful retraining of the tool to cope with the novel diversity presented by this new species. The stomatal complexes in optical tomography images of mature Setaria leaves were accurately identified by comparison to expert raters (R2 = 0.84). This study highlights the challenge of context dependency for widespread application of machine learning tools for phenotyping plant traits, even in closely related species. At the same time, it also provides a new tool that can be applied to leverage Setaria as a model C4 species, while also providing a roadmap for translation of a machine learning to analyze stomatal patterning in new plant species.
Understanding root traits is essential to improve water uptake, increase nitrogen capture and accelerate carbon sequestration from the atmosphere. High-throughput phenotyping to quantify root traits for deeper field-grown roots remains a challenge, however. Recently developed open-source methods use 3D reconstruction algorithms to build 3D models of plant roots from multiple 2D images and can extract root traits and phenotypes. Most of these methods rely on automated image orientation (Structure from Motion) and dense image matching (Multiple View Stereo) algorithms to produce a 3D point cloud or mesh model from 2D images. Until now the performance of these methods when applied to field-grown roots has not been compared tested commonly used open-source pipelines on a test panel of twelve contrasting maize genotypes grown in real field conditions[2-6]. We compare the 3D point clouds produced in terms of number of points, computation time and model surface density. This comparison study provides insight into the performance of different open-source pipelines for maize root phenotyping and illuminates trade-offs between 3D model quality and performance cost for future high-throughput 3D root phenotyping.
Hyperspectral based prediction of nutrient content in maize leaves Hyperspectral imaging is a promising method to predict crop traits in a high-throughput manner and unlock quantitative genetic studies. A single hyperspectral image can be used to predict several unrelated traits at once using spectral data from 350nm - 2500nm. Researchers have successfully modeled different physiological traits in maize such as vegetative Nitrogen content but the effect of different development stages, genotypes, and treatments on modelling power remains unclear. Here, I explore the ability to model leaf macro- and micro- nutrient content and leaf water content from hyperspectral transmittance data collected with a LeafSpec imaging device. I will compare three different machine learning algorithms; Partial Least Squares Regression, Random Forest and a Convolutional Neural Net to model nutrient content collected from twenty hybrids throughout the 2020 field season in fertilized and not fertilized blocks. Genotypes and development stages excluded from model training are used to externally validate models. Sulfur, Nitrogen, Calcium, Copper, and Iron leaf concentrations were the most amenable nutrients to prediction with coefficient of determination scores from 0.78 - 0.73, respectively. Models trained on samples from a collection of time points were able to accurately predict new time points and genotypes. The findings demonstrate the ability to predict nutrient content in field grown maize over a variety of developmental stages, genotypes, and treatments from a handheld hyperspectral imaging device.
Nitrous oxide (N20) is a greenhouse gas that is three hundred times more potent than carbon dioxide. The majority of N20 emissions worldwide are the result of excess soil nitrogen being metabolized by microbes. It has been hypothesized that crops with better nitrogen uptake efficiency and more roots will reduce excess soil nitrogen therefore reducing N20 emissions. To test this hypothesis, a pilot study was performed in 2021 in collaboration with Iowa State University in which root growth dynamics were captured using RootTracker ™ technology in four commercial maize hybrids. This preliminary study showed a correlation between increased root growth and reduced N20 emissions. Further, we find genetic differences in root growth that is consistent across reps, suggesting that i) cultivar choice impacts N2O emissions and ii) that it is possible to breed for root system architecture to limit N2O emissions. It was also observed that the hybrid with the fastest rate of root growth (lowest N20 emissions) did not reach the greatest soil depth, suggesting early root establishment could be pivotal to more efficient nitrogen uptake. These preliminary results suggest there are differences in root growth by variety that could be exploited to reduce agricultural N20 emissions at scale.
The PRISM Data Library (DL) is designed to optimize the display, analysis, and retrieval of multiple domains datasets. Originally created for climate data, we aggregated data from agriculture and hydrology domains, as well as non-traditional domains for the DL such as ecology, finance, power outage and space weather data. These datasets range from simple geospatial point observations, to spatially gridded data products, to high-resolution satellite measurements, to GIS representation of administrative or domain-specific geographic entities. These datasets are represented in a consistent multi-dimensional (most often spatial and temporal) framework. As a result, dimension-wise comparisons are easily enabled through selection or transformation. Gridded data can be averaged over discrete geometrical entities (e.g. Counties, Bird Conservation Regions). The DL can be used in a browser, by connecting to servers at San Diego Supercomputing Center (SDSC) over the internet. Data selection, processing, and analysis are performed by the SDSC DL servers, and the resulting images or data files are sent back to the client’s desktop. This model optimizes the use of internet bandwidth.
The symbiosis between crops and arbuscular mycorrhizal fungi (AMF) have become an attractive route towards achieving carbon neutral agriculture and reducing the use of chemical fertilizers. Yet, our understanding of how active AMF infections influence the uptake, allocation, and exchange of carbon is limited. Here, we combine X-ray CT and PET imaging to observe and quantify the flow of carbon from leaves to roots to hyphae. Comparison of maize grown with and without AMF allows us to measure changes in the amount of 11CO2 taken up in leaves and subsequently the amount of 11C allocated to below-ground roots. Then, co-registered CT and PET images are used to identify hot spots which may indicate active AMF infection sites. Finally, analysis of 11C kinetics at these hot spots are used to assess the amount of carbon exchanged between maize roots and hyphae. By combining structural and biochemical information, we begin to deepen our understanding of the different types of changes in carbon flow in Maize-AMF systems and how we can improve sustainable agriculture efforts.
Producers desire varieties that consistently perform with high yields and end-use qualities. Unlike easily recognized average yield improvements, yield stability over time is less examined, especially when considering the role of breeding relative to other factors like management and changing climatic conditions. Our study system was a 70-year historical dataset from which we estimated the year-over-year stability of Triticum aestivum, winter wheat varieties released by Montana's Agricultural Experimental Station. We examined yield stability within six locations representing diverse growing conditions across Montana and found evidence that breeding has improved stability at specific locations and not at others. Newer varieties showed improved year-over-year stability at locations that tended to have the lowest yields and more extreme weather conditions, reflecting that year-over-year stability has a genotype-by-environment component. We examined the role of climatic conditions, including temperature and rainfall to understand if reduced climatic variability was driving patterns of improved stability at these sites. However, the impact of breeding remained, or became evident when accounting for climatic variables. Together, these findings suggest that breeding's strong selective pressures improve second order traits.
OpenET is a software system that makes satellite-based multi-model estimates of evapotranspiration (ET) accessible at multiple spatial and temporal scales over the U.S. Large-scale ET estimates fill a critical data-gap for irrigation management, water resources management, and hydrological modeling and research. We present the methods and results of the second phase of an intercomparison and accuracy assessment between OpenET satellite-based models (ALEXI/DisALEXI, eeMETRIC, PT-JPL, geeSEBAL, SIMS and SSEBop) and a benchmark ground-based ET dataset with data from nearly 200 eddy covariance towers across the contiguous U.S. Processing steps for the benchmark dataset included gap-filling, energy balance closure correction, calculation of closed and unclosed daily ET, and multiple levels of data QA/QC. The dataset was split into three groups, phase I and II of the intercomparison and a reserve dataset for future studies. To sample satellite-based ET pixels, static flux footprints were generated at each station based on dominant wind speed and direction. Where data allowed, two dimensional flux footprints that are weighted by hourly ETo were developed and used for ET pixel sampling. A wide range of visual and statistical comparisons between satellite and ground-based ET were conducted at each station and against stations grouped by land cover type. Based on key performance metrics including bias, coefficient of determination, and root mean square error, model results show promising agreement at many flux sites considering the inherent uncertainty in station data. Remote sensing models show the highest agreement with closed station ET in irrigated annual cropland settings whereas locations of native vegetation with high aridity and some forested stations show relatively less agreement. The benchmark ET dataset was used to explore different approaches to computing a single ensemble estimate from the six model ensemble, with the goal of reducing the influence of model outliers and selection of weighting and data sampling schemes to reduce the influence of flux stations with sparse or extensive data records. We present the results from the model intercomparison and accuracy assessment and discuss model performance relative to accuracy requirements from the OpenET user community.
To satisfy increasing global agricultural demand, the expansion of irrigation is an important intensification measure. At the same time, unsustainable water abstractions and cropland expansion pose a threat to biodiversity and ecosystem functioning. Irrigation potentials are influenced by local biophysical irrigation water availability and competition of different water users. Because water abstractions for various human uses along the river divert the river flow, it is also important to consider competing water uses when estimating irrigation potentials. Using a novel river routing routine that considers economic criteria of water allocation via a productivity ranking of grid cells and both land and water sustainability criteria, we estimate global irrigation potentials at a halfdegree spatial resolution. We show that there are considerable potentials to expand irrigation without harming the environment, but not necessarily at the places where irrigation is taking place today. In terms of potentially irrigated areas on current cropland, 711 Mha could be sustainably irrigated when only considering biophysical criteria. Of these, only 254 Mha have a yield value gain of more than 500 USD/ha and would be economically viable to be irrigated. The open-source data processing routine is a valuable aggregation and disaggregation tool for the use of hydrological inputs within land-system models that do not have a highly resolved representation of land use. The potentials can be aggregated to different simulation level units (e.g. basin level or country level) while maintaining biophysical and economic consistency.
Climate patterns in the agricultural zones of the Indus basin are predicted to undergo undesirable changes in the hydrological cycle. These changes are a threat to the widespread agricultural activity and associated livelihoods of the underlying population. Livestock, an essential sector for human sustenance in the basin, is also a major source of greenhouse gas emissions thereby contributing towards climate change. However, it is also a recipient of climate impacts, thus introducing feedbacks and uncertainties that are further accentuated by the Water-Energy-Food Nexus. Here we model and simulate the farm-level dairy operations of a single dairy farm by introducing informatics-driven precision measurements of water, energy, food, and carbon emissions in a system dynamics framework. We analyze the simulated trajectories for energy, water, and waste fluxes to under different interventive scenarios to identify actions that enhance productivity and minimize environmental impact. The model is constructed based on data gathered from two dairy farms located in rural Punjab, Pakistan. The farms have a livestock capacity of 300 and 134 animals respectively, with data related to water, energy, food, and climate gathered over a duration of two years. The simulated results may be used to uncover structural changes in dairy-farm operations which improve the economic structure of the farm while remining within the thresholds defined by Sustainable Development Goals (SDG) 3, 7 and 13 set by the United Nations. The model itself also helps in unravelling the complex interactions among water-energy-food flows along with their coupling to land-climate interactions in context of the dairy farm operations. Beyond the climate change adaptation measures extracted from this study, the system dynamics model that we construct in the process, can help develop economic tools that leverage the advantages of water/climate informatics driven data services and decisions under large variabilities to devise sound agricultural policy.
The increase of vegetation greenness in the Northern latitudes suggests a rise in the fixation of CO2 by photosynthesis, but the observed upward trends in respiration could compensate for elevated uptake by photosynthesis, necessitating the monitoring of variation in vegetation structure and carbon (C) storage at very high spatio-temporal resolution. Compared to passive optical remote sensing, Light Detection and Ranging (Lidar) scanners may improve the quantification of C sink by providing 3D information of plant structures without apparent sign of saturation of spectral response over dense canopies. We evaluate a novel approach to precisely map C sequestration and key metrics describing the 3D canopy structure of a temperate agricultural expanse by implementing drone-borne Lidar scanner technology and deep learning (DL) architectures potentially capable of detecting individual plants and associated geometrical properties while deriving their above ground biomass (AGB) from point cloud datasets originating from the scanner. An intensive aerial and field campaign was carried out over an Integrated Carbon Observation System (ICOS) class 1 station site (60 ha) in Denmark to remotely measure the horizontal and vertical canopy structure at 15-day intervals during the vegetation growing period, and to collect ground truth data of crop growth in terms of height, density, AGB and green area index of more than 1200 plants. The point cloud data are processed using pattern recognition tools to remove noise and classify them to ground and non-ground points. Two DL models specifically designed to handle the irregular structure of raw point clouds are trained to extract features of vegetation by labeling the processed point cloud data; DL’s suitability for assigning semantic information on 3D data representing cropland is assessed by validating them with the field-based observations. In combination with tower-based flux data, the application of Lidar and DL technologies appear to offer a characterization of the dynamic interaction between climatic conditions, vegetation growth, C sink, water and CO2 fluxes suitable to the challenge of assessing the rapidly changing northern landscapes.
Genomic selection (GS) can improve the efficiency of tea breeding compared to phenotypic selection (PS) by shortening the generation interval, increasing selection accuracy, and shortening the duration of the entire breeding program, especially at early stages. Tea (Camellia sinensis (L.) O. Kuntze) is mainly grown in low- to middle-income countries (LMIC) and is a global commodity. Breeding programs in these countries face the challenge of increasing genetic gain because the accuracy of selecting superior genotypes is low and resources are limited. Recurrent phenotypic selection has traditionally been the primary method for developing improved tea varieties and can take over 16 years. Therefore, the main objective of this study was to investigate the potential of implementing GS in tea breeding programs to speed up genetic progress despite the low labour costs in LMIC. We used stochastic simulations to compare three GS breeding programs with a commercial PS program over a 40-year breeding period. All GS breeding programs achieved higher genetic gains compared to PS. Seed-GSconst, in particular, proved to be the most cost-effective strategy for introducing GS into tea breeding programs. It introduces GS at the nursery stage, thereby increasing the predictive accuracy at the early stage of the breeding program. It also shortens the duration of the entire breeding program by three years and reduces the generation interval to two years. Our results indicate that GS is a promising strategy to improve genetic gain per unit time and cost in tea breeding programs.
Improving photosynthesis has been considered critical to increasing crop yield to meet food demands from a growing population. To achieve this goal, high-throughput phenotyping techniques are highly needed to explore both natural and genetic variation in photosynthetic performance among crop cultivars. Due to the non-invasive nature of hyperspectral imaging, there is an increasing use of hyperspectral imaging for phenotyping of photosynthesis or photosynthetic physiology. The use of hyperspectral sensors has resulted in the accumulation of large amounts of data, shifting the research efforts into efficiently mining spectral information for high-throughput phenotyping. In this presentation, we will introduce data pipelines developed to leverage proximal sensing platforms and data sources including both reflectance spectra and solar-induced fluorescence (SIF) for quantifying photosynthetic performance at the canopy level. Photosynthetic performance was represented by the maximum carboxylation rate (Vcmax) and the maximum electron transport rate (Jmax). The experiments were conducted using eleven tobacco cultivars grown in field conditions during 2017 and 2018 at Energy Farm at University of Illinois. Time-synchronized hyperspectral images from 400 to 900 nm and irradiance measurements of sunlight under clear-sky conditions were collected for capturing reflectance spectra and SIF (and SIF related parameters). Within 30 minutes of spectral measurements, ground-truth Vcmax and Jmax were obtained from portable leaf gas exchange system. Our results suggested both reflectance spectra and SIF can provide accurate estimations of Vcmax and Jmax. The presented data pipelines have potential to relieve bottleneck in phenotyping of photosynthesis for breeding cultivars of enhanced photosynthesis.
Because climate change is both a physical and social phenomenon, personal experience has been considered the first step to entail how individuals perceive climate change risk and which actions can be promoted to reduce their vulnerability. Considering that agriculture is affected by climate change in several ways, farmers can provide first-hand observations of climate change impacts and suggest better adaptation options. However, modeling farmers’ behavior is a non-trivial task: personal experience is well recognized as a complex non-linear, multi-variate process due to the high heterogeneity and uncertainties in human cognition and decision-making processes. Furthermore, individual understandings of climate change are always contextualized within broader considerations, meaning that farmers are not ‘blank slates’ receiving information about climate change, but that information is always and inevitably filtered through values and worldviews. Despite the burgeoning of research on climate change, information about farmers’ awareness and risk perception is not geographically homogenized and varies substantially among countries and regions. For example, studies from Global North regions are scarce and emphasize how farmers characterize themselves rather than how they perceive and react to climate change. Drawing on farmers’ surveys in the Lombardy region (Italy), we provide an empirical study to pre-test the triple-loop analysis of farmers’ behavior regarding climate change: awareness, perceived impacts, and adaptation measures and barriers. Applying descriptive statistics and considering socio-economic data and farm characteristics, we address two main research questions: 1) What are farmers’ perceptions of climatic impacts and which responses do they promote? 2) How do personal experience and attitude change is conditioning farmers’ adaptation capacity? Obtained results from accurate bottom-up knowledge on farmers’ behavior may increase policy-makers’ and managers’ understanding of climate change and re-think local policies, which is essential to address agricultural risks in climate change hotspots.