Assessing agricultural drought in summer over Oklahoma Mesonet sites using the water-related vegetation index from MODIS.

Agricultural drought, a common phenomenon in most parts of the world, is one of the most challenging natural hazards to monitor effectively. Land surface water index (LSWI), calculated as a normalized ratio between near infrared (NIR) and short-wave infrared (SWIR), is sensitive to vegetation and soil water content. This study examined the potential of a LSWI-based, drought-monitoring algorithm to assess summer drought over 113 Oklahoma Mesonet stations comprising various land cover and soil types in Oklahoma. Drought duration in a year was determined by the number of days with LSWI <0 (DNLSWI) during summer months (June-August). Summer rainfall anomalies and LSWI anomalies followed a similar seasonal dynamics and showed strong correlations (r 2 = 0.62-0.73) during drought years (2001, 2006, 2011, and 2012). The DNLSWI tracked the east-west gradient of summer rainfall in Oklahoma. Drought intensity increased with increasing duration of DNLSWI, and the intensity increased rapidly when DNLSWI was more than 48 days. The comparison between LSWI and the US Drought Monitor (USDM) showed a strong linear negative relationship; i.e., higher drought intensity tends to have lower LSWI values and vice versa. However, the agreement between LSWI-based algorithm and USDM indicators varied substantially from 32 % (D 2 class, moderate drought) to 77 % (0 and D 0 class, no drought) for different drought intensity classes and varied from ∼30 % (western Oklahoma) to >80 % (eastern Oklahoma) across regions. Our results illustrated that drought intensity thresholds can be established by counting DNLSWI (in days) and used as a simple complementary tool in several drought applications for semi-arid and semi-humid regions of Oklahoma. However, larger discrepancies between USDM and the LSWI-based algorithm in arid regions of western Oklahoma suggest the requirement of further adjustment in the algorithm for its application in arid regions.

Comparison of solar-induced chlorophyll fluorescence, light-use efficiency, and process-based GPP models in maize.

Accurately quantifying cropland gross primary production (GPP) is of great importance to monitor cropland status and carbon budgets. Satellite-based light-use efficiency (LUE) models and process-based terrestrial biosphere models (TBMs) have been widely used to quantify cropland GPP at different scales in past decades. However, model estimates of GPP are still subject to large uncertainties, especially for croplands. More recently, space-borne solar-induced chlorophyll fluorescence (SIF) has shown the ability to monitor photosynthesis from space, providing new insights into actual photosynthesis monitoring. In this study, we examined the potential of SIF data to describe maize phenology and evaluated three GPP modeling approaches (space-borne SIF retrievals, a LUE-based vegetation photosynthesis model [VPM], and a process-based soil canopy observation of photochemistry and energy flux [SCOPE] model constrained by SIF) at a maize (Zea mays L.) site in Mead, Nebraska, USA. The result shows that SIF captured the seasonal variations (particularly during the early and late growing season) of tower-derived GPP (GPP_EC) much better than did satellite-based vegetation indices (enhanced vegetation index [EVI] and land surface water index [LSWI]). Consequently, SIF was strongly correlated with GPP_EC than were EVI and LSWI. Evaluation of GPP estimates against GPP_EC during the growing season demonstrated that all three modeling approaches provided reasonable estimates of maize GPP, with Pearson's correlation coefficients (r) of 0.97, 0.94, and 0.93 for the SCOPE, VPM, and SIF models, respectively. The SCOPE model provided the best simulation of maize GPP when SIF observations were incorporated through optimizing the key parameter of maximum carboxylation capacity (Vcmax). Our results illustrate the potential of SIF data to offer an additional way to investigate the seasonality of photosynthetic activity, to constrain process-based models for improving GPP estimates, and to reasonably estimate GPP by integrating SIF and GPP_EC data without dependency on climate inputs and satellite-based vegetation indices.

Eddy covariance assessment of alternate wetting and drying floodwater management on rice methane emissions.

Reducing methane emissions and water use is critical for combating climate change and declining aquifers on food production. Reductions in irrigation water use and methane emissions are known benefits of practicing alternate wetting and drying (AWD) over continuous flooding (CF) water management in lowland rice (Oryza sativa L.) production systems. In a two-year (2020 and 2021) study, methane emissions from large farm-scale (∼50 ha) rice fields managed under CF and AWD in soils dominated by Sharkey clay (Sharkey clay, clay over loamy, montmorillonitic non-acid, thermic Vertic halauepet) were monitored using the eddy covariance method (EC). In the EC system, an open-path laser gas analyzer was used to monitor air methane gas density in the constant flux layer of the atmosphere over the rice-crop canopies. Total water pumped into the field for floodwater management was higher in CF compared to AWD by 24 and 14% in 2020 and 2021, respectively. Considerable variations between seasons in the amount of methane emitted from the CF and AWD treatments were observed: CF emitted 29 and 75 kg ha-1 and AWD emitted 14 and 34 kg ha-1 methane in 2020 and 2021, respectively. Notwithstanding, the extent of reduction in methane emissions due to AWD over CF was similar for each crop season (52% in 2020 and 55% in 2021). Rice grain yield harvested differed by only ±2% between AWD and CF. This investigation of large-scale system-level evaluation, using the EC method, confirmed that by practicing AWD floodwater management in rice, water pumped from aquifers could be reduced by about a quarter and methane emissions from rice fields could be cut down by about half without affecting grain yields, thereby promoting sustainable water management and greenhouse gas emission reduction during rice production in the Lower Mississippi Delta.

Land use conversion increases network complexity and stability of soil microbial communities in a temperate grassland.

Soils harbor highly diverse microbial communities that are critical to soil health, but agriculture has caused extensive land use conversion resulting in negative effects on critical ecosystem processes. However, the responses and adaptations of microbial communities to land use conversion have not yet been understood. Here, we examined the effects of land conversion for long-term crop use on the network complexity and stability of soil microbial communities over 19 months. Despite reduced microbial biodiversity in comparison with native tallgrass prairie, conventionally tilled (CT) cropland significantly increased network complexity such as connectivity, connectance, average clustering coefficient, relative modularity, and the number of species acting at network hubs and connectors as well as resulted in greater temporal variation of complexity indices. Molecular ecological networks under CT cropland became significantly more robust and less vulnerable, overall increasing network stability. The relationship between network complexity and stability was also substantially strengthened due to land use conversion. Lastly, CT cropland decreased the number of relationships between network structure and environmental properties instead being strongly correlated to management disturbances. These results indicate that agricultural disturbance generally increases the complexity and stability of species "interactions", possibly as a trade-off for biodiversity loss to support ecosystem function when faced with frequent agricultural disturbance.

Indicators of water use efficiency across diverse agroecosystems and spatiotemporal scales.

Understanding the relationship between water and production within and across agroecosystems is essential for addressing several agricultural challenges of the 21st century: providing food, fuel, and fiber to a growing human population, reducing the environmental impacts of agricultural production, and adapting food systems to climate change. Of all human activities, agriculture has the highest demand for water globally. Therefore, increasing water use efficiency (WUE), or producing 'more crop per drop', has been a long-term goal of agricultural management, engineering, and crop breeding. WUE is a widely used term applied across a diverse array of spatial scales, spanning from the leaf to the globe, and over temporal scales ranging from seconds to months to years. The measurement, interpretation, and complexity of WUE varies enormously across these spatial and temporal scales, challenging comparisons within and across diverse agroecosystems. The goals of this review are to evaluate common indicators of WUE in agricultural production and assess tradeoffs when applying these indicators within and across agroecosystems amidst a changing climate. We examine three questions: (1) what are the uses and limitations of common WUE indicators, (2) how can WUE indicators be applied within and across agroecosystems, and (3) how can WUE indicators help adapt agriculture to climate change? Addressing these agricultural challenges will require land managers, producers, policy makers, researchers, and consumers to evaluate costs and benefits of practices and innovations of water use in agricultural production. Clearly defining and interpreting WUE in the most scale-appropriate way is crucial for advancing agroecosystem sustainability.

Temporal Dynamics of Bacterial Communities along a Gradient of Disturbance in a U.S. Southern Plains Agroecosystem.

Land conversion for intensive agriculture produces unfavorable changes to soil ecosystems, causing global concern. Soil bacterial communities mediate essential terrestrial ecosystem processes, making it imperative to understand their responses to agricultural perturbations. Here, we used high-throughput sequencing coupled with a functional gene array to study temporal dynamics of soil bacterial communities over 1 year under different disturbance intensities across a U.S. Southern Plains agroecosystem, including tallgrass prairie, Old World bluestem pasture, no-tillage (NT) canola, and conventional tillage (CT) wheat. Land use had the greatest impact on bacterial taxonomic diversity, whereas sampling time and its interaction with land use were central to functional diversity differences. The main drivers of taxonomic diversity were tillage > sampling time > temperature, while all measured factors explained similar amounts of variations in functional diversity. Temporal differences had the strongest correlation with total nitrogen > rainfall > nitrate. Within land uses, community variations for CT wheat were attributed to nitrogen levels, whereas soil organic matter and soil water content explained community variations for NT canola. In comparison, all measured factors contributed almost equally to variations in grassland bacterial communities. Finally, functional diversity had a stronger relationship with taxonomic diversity for CT wheat compared to phylogenetic diversity in the prairie. These findings reinforce that tillage management has the greatest impact on bacterial community diversity, with sampling time also critical. Hence, our study highlights the importance of the interaction between temporal dynamics and land use in influencing soil microbiomes, providing support for reducing agricultural disturbance to conserve soil biodiversity. IMPORTANCE Agricultural sustainability relies on healthy soils and microbial diversity. Agricultural management alters soil conditions and further influences the temporal dynamics of soil microbial communities essential to ecosystem functions, including organic matter dynamics, nutrient cycling, and plant nutrient availability. Yet, the responses to agricultural management are also dependent on soil type and climatic region, emphasizing the importance of assessing sustainability at local scales. To evaluate the impact of agricultural management practices, we examined bacterial communities across a management disturbance gradient over 1 year in a U.S. Southern Plains agroecosystem and determined that intensive management disturbance and sampling time critically impacted bacterial structural diversity, while their interactive effect influenced functional diversity and other soil health indicators. Overall, this study provides insights into how reducing soil disturbance can positively impact microbial community diversity and soil properties in the U.S. Southern Plains.

Temporal Changes of Virus-Like Particle Abundance and Metagenomic Comparison of Viral Communities in Cropland and Prairie Soils.

During the last several decades, viruses have been increasingly recognized for their abundance, ubiquity, and important roles in different ecosystems. Despite known contributions to aquatic systems, few studies examine viral abundance and community structure over time in terrestrial ecosystems. The effects of land conversion and land management on soil microbes have been previously investigated, but their effects on virus population are not well studied. This study examined annual dynamics of viral abundance in soils from a native tallgrass prairie and two croplands, conventional till winter wheat and no-till canola, in Oklahoma. Virus-like particle (VLP) abundance varied across sites, and showed clear seasonal shifts. VLP abundance significantly correlated with environmental variables that were generally reflective of land use, including air temperature, soil nitrogen, and plant canopy coverage. Structural equation modeling supported the effects of land use on soil communities by emphasizing interactions between management, environmental factors, and viral and bacterial abundance. Between the viral metagenomes from the prairie and tilled wheat field, 1,231 unique viral operational taxonomic units (vOTUs) were identified, and only five were shared that were rare in the contrasting field. Only 13% of the vOTUs had similarity to previously identified viruses in the RefSeq database, with only 7% having known taxonomic classification. Together, our findings indicated land use and tillage practices influence virus abundance and community structure. Analyses of viromes over time and space are vital to viral ecology in providing insight on viral communities and key information on interactions between viruses, their microbial hosts, and the environment. IMPORTANCE Conversion of land alters the physiochemical and biological environments by not only changing the aboveground community, but also modifying the soil environment for viruses and microbes. Soil microbial communities are critical to nutrient cycling, carbon mineralization, and soil quality; and viruses are known for influencing microbial abundance, community structure, and evolution. Therefore, viruses are considered an important part of soil functions in terrestrial ecosystems. In aquatic environments, virus abundance generally exceeds bacterial counts by an order of magnitude, and they are thought to be one of the greatest genetic reservoirs on the planet. However, data are extremely limited on viruses in soils, and even less is known about their responses to the disturbances associated with land use and management. The study provides important insights into the temporal dynamics of viral abundance and the structure of viral communities in response to the common practice of turning native habitats into arable soils.

Improving a Biogeochemical Model to Simulate Microbial-Mediated Carbon Dynamics in Agricultural Ecosystems.

Soil microbes drive decomposition of soil organic matter (SOM) and regulate soil carbon (C) dynamics. Process-based models have been developed to quantify changes in soil organic carbon (SOC) and carbon dioxide (CO2) fluxes in agricultural ecosystems. However, microbial processes related to SOM decomposition have not been, or are inadequately, represented in these models, limiting predictions of SOC responses to changes in microbial activities. In this study, we developed a microbial-mediated decomposition model based on a widely used biogeochemical model, DeNitrification-DeComposition (DNDC), to simulate C dynamics in agricultural ecosystems. The model simulates organic matter decomposition, soil respiration, and SOC formation by simulating microbial and enzyme dynamics and their controls on decomposition, and considering impacts of climate, soil, crop, and farming management practices (FMPs) on C dynamics. When evaluated against field observations of net ecosystem CO2 exchange (NEE) and SOC change in two winter wheat systems, the model successfully captured both NEE and SOC changes under different FMPs. Inclusion of microbial processes improved the model's performance in simulating peak CO2 fluxes induced by residue return, primarily by capturing priming effects of residue inputs. We also investigated impacts of microbial physiology, SOM, and FMPs on soil C dynamics. Our results demonstrated that residue or manure input drove microbial activity and predominantly regulated the CO2 fluxes, and manure amendment largely regulated long-term SOC change. The microbial physiology had considerable impacts on the microbial activities and soil C dynamics, emphasizing the necessity of considering microbial physiology and activities when assessing soil C dynamics in agricultural ecosystems.

Integrating eddy fluxes and remote sensing products in a rotational grazing native tallgrass prairie pasture.

Eddy covariance (EC) systems provide integrated fluxes within their footprint areas. Spatial heterogeneity of up-scaled areas and spatio-temporal mismatches between EC footprint and remote sensing pixels jeopardize the performance of most satellite-based models. To examine the impact of spatial resolution of satellite products on up-scaling of fluxes, we compared the relationships between measured eddy fluxes and enhanced vegetation index (EVI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) at 500 and 250 m spatial resolutions, Visible Infrared Imaging Radiometer Suite (VIIRS) at 500 m spatial resolution, and Landsat at 30 m spatial resolution but integrated at the paddock-scale. The experiment was conducted over a grazed native tallgrass prairie pasture, which was divided into nine paddocks for rotational grazing. The EVI data from all satellites showed consistency in detecting vegetation phenology. Seasonality of EC-measured fluxes corresponded well with remotely-sensed vegetation phenology. Approximately 80% of contribution to eddy fluxes came from within 80 m upwind distance of the 2.7 m tall EC tower. As a result, the major contributing area for the measured fluxes was mostly limited to the paddock containing the EC tower. Different timings and duration of grazing caused some heterogeneity among paddocks within the pasture. The EVI of different spatial scales showed strong relationships with CO2 fluxes. However, Landsat-derived EVI integrated for the paddock containing the EC tower showed substantially stronger relationships with CO2 fluxes than did MODIS and VIIRS-derived EVI integrated for multiple paddocks, most likely due to similar spatial resolutions of remote sensing and EC observations. Results illustrate that satellite products of fine-scale spatial resolution that are comparable to EC footprints can help improve the performance of satellite-based models for modeling or up-scaling of eddy fluxes, especially in heterogeneous ecosystems.

Dynamics of CO2 and H2O fluxes in Johnson grass in the U.S. Southern Great Plains.

Johnson grass (Sorghum halepense (L.) Pers.) is rapidly spreading throughout the continental United States (U.S.). Thus, determining magnitudes and seasonal dynamics of carbon dioxide (CO2) and water vapor (H2O) fluxes in Johnson grass is crucial to understand regional changes in hydrology and carbon balance. Using eddy covariance (EC), CO2 and H2O fluxes were measured from June 2017 to October 2019 over a rainfed Johnson grass field in central Oklahoma. Hay was harvested from late May to early July each year, with biomass yield ~7.5 t ha-1. Weekly averaged daily integrated net ecosystem CO2 exchange (NEE), gross primary production (GPP), and evapotranspiration (ET) reached -8.28 ± 0.76 g C m-2, 20.02 ± 1.62 g C m-2, and 5.42 ± 0.26 mm, respectively. Ecosystem water use efficiency (EWUE) and ecosystem light use efficiency (ELUE) ranged from 3.22 to 3.93 g C mm-1 ET and 0.34 to 0.41 g C mol-1 PAR (photosynthetically active radiation), respectively, during peak growths. Based on aggregated fluxes for each month over the three years (2017-2019), cumulative annual NEE was -434 ± 112 g C m-2, indicating a carbon gain by the Johnson grass field. Cumulative annual ET (858 ± 72 mm) was ~86% of the average annual rainfall (996 ± 100 mm). Results showed Johnson grass could be a carbon sink from May to September in the U.S. Southern Great Plains. Both NEE and ET did not decline up to air temperature (Ta) of ~33 °C and vapor pressure deficit (VPD) of ~2 kPa, suggesting optimum Ta of ≥33 °C and VPD of ≥2 kPa for the fluxes. Results indicated that Johnson grass might be well suited for dryland production in the region. Additionally, these findings provide initial baseline information on CO2 fluxes and ET for Johnson grass relative to other forage species in the region.

Net ecosystem exchange of CO2 and H2O fluxes from irrigated grain sorghum and maize in the Texas High Plains.

Net ecosystem exchange (NEE) of carbon dioxide (CO2) and water vapor (H2O) fluxes from irrigated grain sorghum (Sorghum bicolor L. Moench) and maize (Zea mays L.) fields in the Texas High Plains were quantified using the eddy covariance (EC) technique during 2014-2016 growing seasons and examined in terms of relevant controlling climatic variables. Eddy covariance measured evapotranspiration (ETEC) was also compared against lysimeter measured ET (ETLys). Daily peak (7-day averages) NEE reached approximately -12 g C m-2 for sorghum and -14.78 g C m-2 for maize. Daily peak (7-day averages) ETEC reached approximately 6.5 mm for sorghum and 7.3 mm for maize. Higher leaf area index (5.7 vs 4-4.5 m2 m-2) and grain yield (14 vs 8-9 t ha-1) of maize compared to sorghum caused larger magnitudes of NEE and ETEC in maize. Comparisons of ETEC and ETLys showed a strong agreement (R2 = 0.93-0.96), while the EC system underestimated ET by 15-24% as compared to lysimeter without any corrections or energy balance adjustments. Both NEE and ETEC were not inhibited by climatic variables during peak photosynthetic period even though diurnal peak values (~2-weeks average) of photosynthetic photon flux density (PPFD), air temperature (Ta), and vapor pressure deficit (VPD) had reached over 2000 µmol m-2 s-1, 30 °C, and 2.5 kPa, respectively, indicating well adaptation of both C4 crops in the Texas High Plains under irrigation. However, more sensitivity of NEE and H2O fluxes beyond threshold Ta and VPD for maize than for sorghum indicated higher adaptability of sorghum for the region. These findings provide baseline information on CO2 fluxes and ET for a minimally studied grain sorghum and offer a robust geographic comparison for maize outside the United States Corn Belt. However, longer-term measurements are required for assessing carbon and water dynamics of these globally important agro-ecosystems.

Carbon dioxide and water vapor fluxes in winter wheat and tallgrass prairie in central Oklahoma.

Winter wheat (Triticum aestivum L.) and tallgrass prairie are common land cover types in the Southern Plains of the United States. During the last century, agricultural expansion into native grasslands was extensive, particularly managed pasture or winter wheat. In this study, we measured carbon dioxide (CO2) and water vapor (H2O) fluxes from winter wheat and tallgrass prairie sites in Central Oklahoma using the eddy covariance in 2015 and 2016. The objective of this study was to contrast CO2 and H2O fluxes between these two ecosystems to provide insights on the impacts of conversion of tallgrass prairie to winter wheat on carbon and water budgets. Daily net ecosystem CO2 exchange (NEE) reached seasonal peaks of -9.4 and -8.8 g C m-2 in 2015 and -6.2 and -7.5 g C m-2 in 2016 at winter wheat and tall grass prairie sites, respectively. Both sites were net sink of carbon during their growing seasons. At the annual scale, the winter wheat site was a net source of carbon (56 ±â€¯13 and 33 ±â€¯9 g C m-2 year-1 in 2015 and 2016, respectively). In contrast, the tallgrass prairie site was a net sink of carbon (-128 ±â€¯69 and -119 ±â€¯53 g C m-2 year-1 in 2015 and 2016, respectively). Daily ET reached seasonal maximums of 6.0 and 5.3 mm day-1 in 2015, and 7.2 and 8.2 mm day-1 in 2016 at the winter wheat and tallgrass prairie sites, respectively. Although ecosystem water use efficiency (EWUE) was higher in winter wheat than in tallgrass prairie at the seasonal scale, summer fallow contributed higher water loss from the wheat site per unit of carbon fixed, resulting into lower EWUE at the annual scale. Results indicate that the differences in magnitudes and patterns of fluxes between the two ecosystems can influence carbon and water budgets.

Growing season variability in carbon dioxide exchange of irrigated and rainfed soybean in the southern United States.

Measurement of carbon dynamics of soybean (Glycine max L.) ecosystems outside Corn Belt of the United States (U.S.) is lacking. This study examines the seasonal variability of net ecosystem CO2 exchange (NEE) and its components (gross primary production, GPP and ecosystem respiration, ER), and relevant controlling environmental factors between rainfed (El Reno, Oklahoma) and irrigated (Stoneville, Mississippi) soybean fields in the southern U.S. during the 2016 growing season. Grain yield was about 1.6tha-1 for rainfed soybean and 4.9tha-1 for irrigated soybean. The magnitudes of diurnal NEE (~2-weeks average) reached seasonal peak values of -23.18 and -34.78µmolm-2s-1 in rainfed and irrigated soybean, respectively, approximately two months after planting (i.e., during peak growth). Similar thresholds of air temperature (Ta, slightly over 30°C) and vapor pressure deficit (VPD, ~2.5kPa) for NEE were observed at both sites. Daily (7-day average) NEE, GPP, and ER reached seasonal peak values of -4.55, 13.54, and 9.95gCm-2d-1 in rainfed soybean and -7.48, 18.13, and 14.93gCm-2d-1 in irrigated soybean, respectively. The growing season (DOY 132-243) NEE, GPP, and ER totals were -54, 783, and 729gCm-2, respectively, in rainfed soybean. Similarly, cumulative NEE, GPP, and ER totals for DOY 163-256 (flux measurement was initiated on DOY 163, missing first 45days after planting) were -291, 1239, and 948gCm-2, respectively, in irrigated soybean. Rainfed soybean was a net carbon sink for only two months, while irrigated soybean appeared to be a net carbon sink for about three months. However, grain yield and the magnitudes and seasonal sums of CO2 fluxes for irrigated soybean in this study were comparable to those for soybean in the U.S. Corn Belt, but they were lower for rainfed soybean.