Predicted changes in future precipitation and air temperature across Bangladesh using CMIP6 GCMs

Understanding spatiotemporal variability in precipitation and temperature and their future projections is critical for assessing environmental hazards and planning long-term mitigation and adaptation. In this study, 18 Global Climate Models (GCMs) from the most recent Coupled Model Intercomparison Project phase 6 (CMIP6) were employed to project the mean annual, seasonal, and monthly precipitation, maximum air temperature (Tmax), and minimum air temperature (Tmin) in Bangladesh. The GCM projections were bias-corrected using the Simple Quantile Mapping (SQM) technique. Using the Multi-Model Ensemble (MME) mean of the bias-corrected dataset, the expected changes for the four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) were evaluated for the near (2015-2044), mid (2045-2074), and far (2075-2100) futures in comparison to the historical period (1985-2014). In the far future, the anticipated average annual precipitation increased by 9.48%, 13.63%, 21.07%, and 30.90%, while the average Tmax (Tmin) rose by 1.09 (1.17), 1.60 (1.91), 2.12 (2.80), and 2.99 (3.69) °C for SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, respectively. According to predictions for the SSP5-8.5 scenario in the distant future, there is expected to be a substantial rise in precipitation (41.98%) during the post-monsoon season. In contrast, winter precipitation was predicted to decrease most (11.12%) in the mid-future for SSP3-7.0, while to increase most (15.62%) in the far-future for SSP1-2.6. Tmax (Tmin) was predicted to rise most in the winter and least in the monsoon for all periods and scenarios. Tmin increased more rapidly than Tmax in all seasons for all SSPs. The projected changes could lead to more frequent and severe flooding, landslides, and negative impacts on human health, agriculture, and ecosystems. The study highlights the need for localized and context-specific adaptation strategies as different regions of Bangladesh will be affected differently by these changes.

Physical and virtual nutrient flows in global telecoupled agricultural trade networks

Global agricultural trade creates multiple telecoupled flows of nitrogen (N) and phosphorus (P). The flows of physical and virtual nutrients along with trade have discrepant effects on natural resources in different countries. However, existing literature has not quantified or analyzed such effects yet. Here we quantified the physical and virtual N and P flows embedded in the global agricultural trade networks from 1997 to 2016 and elaborated components of the telecoupling framework. The N and P flows both increased continuously and more than 25% of global consumption of nutrients in agricultural products were related to physical nutrient flows, while virtual nutrient flows were equivalent to one-third of the nutrients inputs into global agricultural system. These flows have positive telecoupling effects on saving N and P resources at the global scale. Reducing inefficient trade flows will enhance resource conservation, environmental sustainability in the hyper-globalized world.

Assessing Drought and Heat Stress-Induced Changes in the Cotton Leaf Metabolome and Their Relationship With Hyperspectral Reflectance

The study of phenotypes that reveal mechanisms of adaptation to drought and heat stress is crucial for the development of climate resilient crops in the face of climate uncertainty. The leaf metabolome effectively summarizes stress-driven perturbations of the plant physiological status and represents an intermediate phenotype that bridges the plant genome and phenome. The objective of this study was to analyze the effect of water deficit and heat stress on the leaf metabolome of 22 genetically diverse accessions of upland cotton grown in the Arizona low desert over two consecutive years. Results revealed that membrane lipid remodeling was the main leaf mechanism of adaptation to drought. The magnitude of metabolic adaptations to drought, which had an impact on fiber traits, was found to be quantitatively and qualitatively associated with different stress severity levels during the two years of the field trial. Leaf-level hyperspectral reflectance data were also used to predict the leaf metabolite profiles of the cotton accessions. Multivariate statistical models using hyperspectral data accurately estimated (R ² > 0.7 in ∼34% of the metabolites) and predicted (Q ² > 0.5 in 15-25% of the metabolites) many leaf metabolites. Predicted values of metabolites could efficiently discriminate stressed and non-stressed samples and reveal which regions of the reflectance spectrum were the most informative for predictions. Combined together, these findings suggest that hyperspectral sensors can be used for the rapid, non-destructive estimation of leaf metabolites, which can summarize the plant physiological status.

Use of hydraulic traits for modeling genotype-specific acclimation in cotton under drought

Understanding the genetic and physiological basis of abiotic stress tolerance under field conditions is key to varietal crop improvement in the face of climate variability. Here, we investigate dynamic physiological responses to water stress in silico and their relationships to genotypic variation in hydraulic traits of cotton (Gossypium hirsutum), an economically important species for renewable textile fiber production. In conjunction with an ecophysiological process-based model, heterogeneous data (plant hydraulic traits, spatially-distributed soil texture, soil water content and canopy temperature) were used to examine hydraulic characteristics of cotton, evaluate their consequences on whole plant performance under drought, and explore potential genotype × environment effects. Cotton was found to have R-shaped hydraulic vulnerability curves (VCs), which were consistent under drought stress initiated at flowering. Stem VCs, expressed as percent loss of conductivity, differed across genotypes, whereas root VCs did not. Simulation results demonstrated how plant physiological stress can depend on the interaction between soil properties and irrigation management, which in turn affect genotypic rankings of transpiration in a time-dependent manner. Our study shows how a process-based modeling framework can be used to link genotypic variation in hydraulic traits to differential acclimating behaviors under drought.

A data workflow to support plant breeding decisions from a terrestrial field-based high-throughput plant phenotyping system

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.

A high-throughput quantification of resin and rubber contents in Parthenium argentatum using near-infrared (NIR) spectroscopy

BACKGROUND

Guayule (Parthenium argentatum A. Gray), a plant native to semi-arid regions of northern Mexico and southern Texas in the United States, is an alternative source for natural rubber (NR). Rapid screening tools are needed to replace the current labor-intensive and cost-inefficient method for quantifying rubber and resin contents. Near-infrared (NIR) spectroscopy is a promising technique that simplifies and speeds up the quantification procedure without losing precision. In this study, two spectral instruments were used to rapidly quantify resin and rubber contents in 315 ground samples harvested from a guayule germplasm collection grown under different irrigation conditions at Maricopa, AZ. The effects of eight different pretreatment approaches on improving prediction models using partial least squares regression (PLSR) were investigated and compared. Important characteristic wavelengths that contribute to prominent absorbance peaks were identified.

RESULTS

Using two different NIR devices, ASD FieldSpec^®3 performed better than Polychromix Phazir™ in improving R² and residual predicative deviation (RPD) values of PLSR models. Compared to the models based on full-range spectra (750-2500 nm), using a subset of wavelengths (1100-2400 nm) with high sensitivity to guayule rubber and resin contents could lead to better prediction accuracy. The prediction power of the models for quantifying resin content was better than rubber content.

CONCLUSIONS

In summary, the calibrated PLSR models for resin and rubber contents were successfully developed for a diverse guayule germplasm collection and were applied to roughly screen samples in a low-cost and efficient way. This improved efficiency could enable breeders to rapidly screen large guayule populations to identify cultivars that are high in rubber and resin contents.

Energy consumption due to groundwater pumping for irrigation in the North China Plain

Irrigated agriculture consumes a large amount of groundwater resources with a huge energy requirement, and this has seriously restricted the development of green and efficient agriculture in China. However, recent studies on energy consumption for irrigation focus mainly on individual irrigation systems or single wells, and few spatial-temporal energy assessments have been carried out at regional scale. This is needed for effective management of regional energy consumption for groundwater utilization. Based on single-well pumping method, a distributed energy consumption model for groundwater irrigation (DPE_GI) was proposed in this study. The North China Plain (NCP) was selected as the research area, which is a typical groundwater irrigated area and has severe issues with aquifer depletion. The results showed that the average annual energy consumption for groundwater pumping was 13.67 billion kW h, and the energy consumption per area was 1122.4 kW h hm^-2 under the winter wheat - summer maize rotation system in NCP. Current groundwater pumping energy consumption in the NCP is 2.9 times of the initial value in 1986, and the NCP has already become the world's largest energy consumer for groundwater irrigation. Due to the uncertainty of precipitation, energy consumption for irrigation fluctuates per growing season. Groundwater level also impacts energy consumption. Popularizing water-saving irrigation technology such as drip or sprinkler irrigation, changing cropping systems and habits can effectively reduce energy consumption for irrigation.

Estimating parametric phenotypes that determine anthesis date in Zea mays: Challenges in combining ecophysiological models with genetics

Ecophysiological crop models encode intra-species behaviors using parameters that are presumed to summarize genotypic properties of individual lines or cultivars. These genotype-specific parameters (GSP’s) can be interpreted as quantitative traits that can be mapped or otherwise analyzed, as are more conventional traits. The goal of this study was to investigate the estimation of parameters controlling maize anthesis date with the CERES-Maize model, based on 5,266 maize lines from 11 plantings at locations across the eastern United States. High performance computing was used to develop a database of 356 million simulated anthesis dates in response to four CERES-Maize model parameters. Although the resulting estimates showed high predictive value (R2 = 0.94), three issues presented serious challenges for use of GSP’s as traits. First (expressivity), the model was unable to express the observed data for 168 to 3,339 lines (depending on the combination of site-years), many of which ended up sharing the same parameter value irrespective of genetics. Second, for 2,254 lines, the model reproduced the data, but multiple parameter sets were equally effective (equifinality). Third, parameter values were highly dependent (p<10⁻⁶⁹¹⁹) on the sets of environments used to estimate them (instability), calling in to question the assumption that they represent fundamental genetic traits. The issues of expressivity, equifinality and instability must be addressed before the genetic mapping of GSP’s becomes a robust means to help solve the genotype-to-phenotype problem in crops.

Deploying a Proximal Sensing Cart to Identify Drought-Adaptive Traits in Upland Cotton for High-Throughput Phenotyping

Field-based high-throughput phenotyping is an emerging approach to quantify difficult, time-sensitive plant traits in relevant growing conditions. Proximal sensing carts represent an alternative platform to more costly high-clearance tractors for phenotyping dynamic traits in the field. A proximal sensing cart and specifically a deployment protocol, were developed to phenotype traits related to drought tolerance in the field. The cart-sensor package included an infrared thermometer, ultrasonic transducer, multi-spectral reflectance sensor, weather station, and RGB cameras. The cart deployment protocol was evaluated on 35 upland cotton (Gossypium hirsutum L.) entries grown in 2017 at Maricopa, AZ, United States. Experimental plots were grown under well-watered and water-limited conditions using a (0,1) alpha lattice design and evaluated in June and July. Total collection time of the 0.87 hectare field averaged 2 h and 27 min and produced 50.7 MB and 45.7 GB of data from the sensors and RGB cameras, respectively. Canopy temperature, crop water stress index (CWSI), canopy height, normalized difference vegetative index (NDVI), and leaf area index (LAI) differed among entries and showed an interaction with the water regime (p < 0.05). Broad-sense heritability (H²) estimates ranged from 0.097 to 0.574 across all phenotypes and collections. Canopy cover estimated from RGB images increased with counts of established plants (r = 0.747, p = 0.033). Based on the cart-derived phenotypes, three entries were found to have improved drought-adaptive traits compared to a local adapted cultivar. These results indicate that the deployment protocol developed for the cart and sensor package can measure multiple traits rapidly and accurately to characterize complex plant traits under drought conditions.

Nitrogen Management Affects Nitrous Oxide Emissions under Varying Cotton Irrigation Systems in the Desert Southwest, USA

Irrigation of food and fiber crops worldwide continues to increase. Nitrogen (N) from fertilizers is a major source of the potent greenhouse gas nitrous oxide (NO) in irrigated cropping systems. Nitrous oxide emissions data are scarce for crops in the arid western United States. The objective of these studies was to assess the effect of N fertilizer management on NO emissions from furrow-irrigated, overhead sprinkler-irrigated, and subsurface drip-irrigated cotton ( L.) in Maricopa, AZ, on Trix and Casa Grande sandy clay loam soils. Soil test- and canopy-reflectance-based N fertilizer management were compared. In the furrow- and overhead sprinkler-irrigated fields, we also tested the enhanced efficiency N fertilizer additive Agrotain Plus as a NO mitigation tool. Nitrogen fertilizer rates as liquid urea ammonium nitrate ranged from 0 to 233 kg N ha. Two applications of N fertilizer were made with furrow irrigation, three applications under overhead sprinkler irrigation, and 24 fertigations with subsurface drip irrigation. Emissions were measured weekly from May through August with 1-L vented chambers. NO emissions were not agronomically significant, but increased as much as 16-fold following N fertilizer addition compared to zero-N controls. Emission factors ranged from 0.10 to 0.54% of added N fertilizer emitted as NO-N with furrow irrigation, 0.15 to 1.1% with overhead sprinkler irrigation, and <0.1% with subsurface drip irrigation. The reduction of NO emissions due to addition of Agrotain Plus to urea ammonium nitrate was inconsistent. This study provides unique data on NO emissions in arid-land irrigated cotton and illustrates the advantage of subsurface drip irrigation as a low NO source system.

Investigation of the Influence of Leaf Thickness on Canopy Reflectance and Physiological Traits in Upland and Pima Cotton Populations

Many systems for field-based, high-throughput phenotyping (FB-HTP) quantify and characterize the reflected radiation from the crop canopy to derive phenotypes, as well as infer plant function and health status. However, given the technology's nascent status, it remains unknown how biophysical and physiological properties of the plant canopy impact downstream interpretation and application of canopy reflectance data. In that light, we assessed relationships between leaf thickness and several canopy-associated traits, including normalized difference vegetation index (NDVI), which was collected via active reflectance sensors carried on a mobile FB-HTP system, carbon isotope discrimination (CID), and chlorophyll content. To investigate the relationships among traits, two distinct cotton populations, an upland (Gossypium hirsutum L.) recombinant inbred line (RIL) population of 95 lines and a Pima (G. barbadense L.) population composed of 25 diverse cultivars, were evaluated under contrasting irrigation regimes, water-limited (WL) and well-watered (WW) conditions, across 3 years. We detected four quantitative trait loci (QTL) and significant variation in both populations for leaf thickness among genotypes as well as high estimates of broad-sense heritability (on average, above 0.7 for both populations), indicating a strong genetic basis for leaf thickness. Strong phenotypic correlations (maximum r = -0.73) were observed between leaf thickness and NDVI in the Pima population, but not the RIL population. Additionally, estimated genotypic correlations within the RIL population for leaf thickness with CID, chlorophyll content, and nitrogen discrimination ([Formula: see text] = -0.32, 0.48, and 0.40, respectively) were all significant under WW but not WL conditions. Economically important fiber quality traits did not exhibit significant phenotypic or genotypic correlations with canopy traits. Overall, our results support considering variation in leaf thickness as a potential contributing factor to variation in NDVI or other canopy traits measured via proximal sensing, and as a trait that impacts fundamental physiological responses of plants.

Field-Based High-Throughput Plant Phenotyping Reveals the Temporal Patterns of Quantitative Trait Loci Associated with Stress-Responsive Traits in Cotton

The application of high-throughput plant phenotyping (HTPP) to continuously study plant populations under relevant growing conditions creates the possibility to more efficiently dissect the genetic basis of dynamic adaptive traits. Toward this end, we employed a field-based HTPP system that deployed sets of sensors to simultaneously measure canopy temperature, reflectance, and height on a cotton (Gossypium hirsutum L.) recombinant inbred line mapping population. The evaluation trials were conducted under well-watered and water-limited conditions in a replicated field experiment at a hot, arid location in central Arizona, with trait measurements taken at different times on multiple days across 2010-2012. Canopy temperature, normalized difference vegetation index (NDVI), height, and leaf area index (LAI) displayed moderate-to-high broad-sense heritabilities, as well as varied interactions among genotypes with water regime and time of day. Distinct temporal patterns of quantitative trait loci (QTL) expression were mostly observed for canopy temperature and NDVI, and varied across plant developmental stages. In addition, the strength of correlation between HTPP canopy traits and agronomic traits, such as lint yield, displayed a time-dependent relationship. We also found that the genomic position of some QTL controlling HTPP canopy traits were shared with those of QTL identified for agronomic and physiological traits. This work demonstrates the novel use of a field-based HTPP system to study the genetic basis of stress-adaptive traits in cotton, and these results have the potential to facilitate the development of stress-resilient cotton cultivars.

Plant chlorophyll fluorescence: active and passive measurements at canopy and leaf scales with different nitrogen treatments

Most studies assessing chlorophyll fluorescence (ChlF) have examined leaf responses to environmental stress conditions using active techniques. Alternatively, passive techniques are able to measure ChlF at both leaf and canopy scales. However, the measurement principles of both techniques are different, and only a few datasets concerning the relationships between them are reported in the literature. In this study, we investigated the potential for interchanging ChlF measurements using active techniques with passive measurements at different temporal and spatial scales. The ultimate objective was to determine the limits within which active and passive techniques are comparable. The results presented in this study showed that active and passive measurements were highly correlated over the growing season across nitrogen treatments at both canopy and leaf-average scale. At the single-leaf scale, the seasonal relation between techniques was weaker, but still significant. The variability within single-leaf measurements was largely related to leaf heterogeneity associated with variations in CO2 assimilation and stomatal conductance, and less so to variations in leaf chlorophyll content, leaf size or measurement inputs (e.g. light reflected and emitted by the leaf and illumination conditions and leaf spectrum). This uncertainty was exacerbated when single-leaf analysis was limited to a particular day rather than the entire season. We concluded that daily measurements of active and passive ChlF at the single-leaf scale are not comparable. However, canopy and leaf-average active measurements can be used to better understand the daily and seasonal behaviour of passive ChlF measurements. In turn, this can be used to better estimate plant photosynthetic capacity and therefore to provide improved information for crop management.

Hydrologic-Process-Based Soil Texture Classifications for Improved Visualization of Landscape Function

Soils lie at the interface between the atmosphere and the subsurface and are a key component that control ecosystem services, food production, and many other processes at the Earth's surface. There is a long-established convention for identifying and mapping soils by texture. These readily available, georeferenced soil maps and databases are used widely in environmental sciences. Here, we show that these traditional soil classifications can be inappropriate, contributing to bias and uncertainty in applications from slope stability to water resource management. We suggest a new approach to soil classification, with a detailed example from the science of hydrology. Hydrologic simulations based on common meteorological conditions were performed using HYDRUS-1D, spanning textures identified by the United States Department of Agriculture soil texture triangle. We consider these common conditions to be: drainage from saturation, infiltration onto a drained soil, and combined infiltration and drainage events. Using a k-means clustering algorithm, we created soil classifications based on the modeled hydrologic responses of these soils. The hydrologic-process-based classifications were compared to those based on soil texture and a single hydraulic property, Ks. Differences in classifications based on hydrologic response versus soil texture demonstrate that traditional soil texture classification is a poor predictor of hydrologic response. We then developed a QGIS plugin to construct soil maps combining a classification with georeferenced soil data from the Natural Resource Conservation Service. The spatial patterns of hydrologic response were more immediately informative, much simpler, and less ambiguous, for use in applications ranging from trafficability to irrigation management to flood control. The ease with which hydrologic-process-based classifications can be made, along with the improved quantitative predictions of soil responses and visualization of landscape function, suggest that hydrologic-process-based classifications should be incorporated into environmental process models and can be used to define application-specific maps of hydrologic function.

Development and evaluation of a field-based high-throughput phenotyping platform

Physiological and developmental traits that vary over time are difficult to phenotype under relevant growing conditions. In this light, we developed a novel system for phenotyping dynamic traits in the field. System performance was evaluated on 25 Pima cotton (Gossypium barbadense L.) cultivars grown in 2011 at Maricopa, Arizona. Field-grown plants were irrigated under well watered and water-limited conditions, with measurements taken at different times on 3 days in July and August. The system carried four sets of sensors to measure canopy height, reflectance and temperature simultaneously on four adjacent rows, enabling the collection of phenotypic data at a rate of 0.84ha h-1. Measurements of canopy height, normalised difference vegetation index and temperature all showed large differences among cultivars and expected interactions of cultivars with water regime and time of day. Broad-sense heritabilities (H2)were highest for canopy height (H2=0.86-0.96), followed by the more environmentally sensitive normalised difference vegetation index (H2=0.28-0.90) and temperature (H2=0.01-0.90) traits. We also found a strong agreement (r2=0.35-0.82) between values obtained by the system, and values from aerial imagery and manual phenotyping approaches. Taken together, these results confirmed the ability of the phenotyping system to measure multiple traits rapidly and accurately.

Verifiable metamodels for nitrate losses to drains and groundwater in the Corn Belt, USA

Nitrate leaching in the unsaturated zone poses a risk to groundwater, whereas nitrate in tile drainage is conveyed directly to streams. We developed metamodels (MMs) consisting of artificial neural networks to simplify and upscale mechanistic fate and transport models for prediction of nitrate losses by drains and leaching in the Corn Belt, USA. The two final MMs predicted nitrate concentration and flux, respectively, in the shallow subsurface. Because each MM considered both tile drainage and leaching, they represent an integrated approach to vulnerability assessment. The MMs used readily available data comprising farm fertilizer nitrogen (N), weather data, and soil properties as inputs; therefore, they were well suited for regional extrapolation. The MMs effectively related the outputs of the underlying mechanistic model (Root Zone Water Quality Model) to the inputs (R(2) = 0.986 for the nitrate concentration MM). Predicted nitrate concentration was compared with measured nitrate in 38 samples of recently recharged groundwater, yielding a Pearson's r of 0.466 (p = 0.003). Predicted nitrate generally was higher than that measured in groundwater, possibly as a result of the time-lag for modern recharge to reach well screens, denitrification in groundwater, or interception of recharge by tile drains. In a qualitative comparison, predicted nitrate concentration also compared favorably with results from a previous regression model that predicted total N in streams.