Net ecosystem exchange of carbon dioxide and water vapor fluxes in switchgrass and high biomass sorghum

Understanding carbon and water dynamics of switchgrass (Panicum virgatum L.) and high biomass sorghum ((Sorghum bicolor L. Moench) ecosystems is crucial as the acreage of these feedstocks is expanding for cellulosic biofuels. Net ecosystem exchange (NEE) of CO2 and H2O was measured using eddy covariance system over co-located switchgrass and sorghum fields in south central Oklahoma, USA. The major objectives of this study were to quantify and examine seasonal variations in NEE, evapotranspiration (ET), and ecosystem water use efficiency (EWUE) over switchgrass and sorghum ecosystems in response to controlling factors, and to explore the underlying mechanisms. The results revealed photosynthetic photon flux density (PPFD) as the most significant environmental factor for variation in NEE under optimal weather conditions. However, warm air temperature and high vapor pressure deficit (VPD) obscured the NEE-PPFD relationship. Larger VPD (>3 kPa) limited photosynthesis and asymmetrical diurnal NEE cycles were observed in both ecosystems. Consequently, rectangular hyperbolic light-response curve (NEE partitioning algorithm) consistently failed to provide good fits at high VPD. Modified rectangular hyperbolic light-VPD response model accounted for the limitation of VPD on photosynthesis and improved the model performance significantly. The magnitudes of CO2 and H2O fluxes were similar in both ecosystems during the active growing periods and the differences in carbon sink potential and seasonal water demand were primarily driven by the length of the growing season. Monthly ensemble averaged NEE of switchgrass and sorghum reached seasonal peak values of -33.02 +- 1.96 and -35.86 +- 2.32 micromol m-2 s-1, respectively. Similarly, weekly average of daily integrated NEE reached seasonal peaks of -8.5 g C m -2 day-1 in switchgrass and -10.3 g C m-2 day-1 in sorghum. During peak growth, daily ET reached up to 6.2 mm day &minus1 for switchgrass and 6.7 mm day-1 for sorghum. The EWUE was about 12 g CO2 mm-1 ET in switchgrass and about 10 g CO2 mm-1 ET in sorghum. This research showed strong seasonal carbon sink potential and high water use efficiency of both ecosystems in this region. However, evaluation over a longer term would be more valuable

Foliar Application of Nickel and/or Copper on Pecan Performance in Container and the Field

Nickel (Ni) shortage is more likely for ureide-nitrogen transporting crops like pecans [Carya illinoinensis (Wang.) K. Koch]. Soil pH in the Red River basin is near neutral or alkaline restricting the availability of many nutrient cations such as zinc (Zn), copper (Cu) and Ni. Nickel and/or Cu treatments were applied as a foliar application at the parachute stage of leaf development followed by two additional applications at two week intervals. The objectives of the study were to 1) evaluate the interaction between (nitrogen) N rate and foliar Ni application on growth of container-grown pecan seedlings, 2) determine the response of bearing pecan trees to foliar Ni application, and 3) assess the impact of foliar Ni and Cu treatments to mitigate the problems associated with excess N. Leaf Ni concentration of 3 μg g-1 dry weight met pecan tree's Ni requirement if leaf Zn and Cu concentrations were within or near the established sufficiency ranges. No benefits from additional Ni beyond the critical level were detected on tree yield, nut quality, tree growth, and N metabolism. Positive responses to essential elements are common when an element is in short supply but visible symptoms are not present. This is termed "hidden hunger" in many texts. Our results indicate that the range between hidden hunger and becoming symptomatic is narrow for Ni. It was hypothesized that excess N may be exacerbating kernel necrosis, necrotic tissues at the basal end of kernel, at Tim Montz's orchard where trees derive an unusually high amount of N from nitrate contaminated irrigation water. The supplemental applications of Ni and Cu may enhance growth and production in this orchard via improved Ni and Cu nutrition and by mitigating any negative effects associated with excess N. Although Ni and Cu treatments reduced leaf N, kernel necrosis was unaffected by treatment. The cause of kernel necrosis remains unknown. The effects of Ni and/or Cu treatments on tree yield, nut quality, and tree growth were inconsistent.Horticulture and Landscape Architecture Departmen

Varietal evaluation of promising maize genotypes in mid hills of Nepal

The varietal evaluation of hybrid maize (Zea mays L.) genotypes with desired performance is one of the main objectives of maize breeding program. Fourteen hybrid maize genotypes were evaluated for 17 quantitative and nine qualitative traits in randomized complete block design with three replications at Sundarbazar, Lamjung, Nepal during May to September, 2019. The major objective was to identify superior genotypes based on genotypic and phenotypic variability, heritability, genetic advance, and correlation between grain yield and yield associated traits. We observed significant differences for 17 quantitative traits among the tested genotypes. Large variation was observed for grain yield among genotypes. Genotype RL-24-0/ RL-111 had the lowest yield (5.53 mt/ha) and Pioneer had the highest yield (11.98 mt/ha) whereas check variety Rampur Hybrid-10 yielded of 8.23 mt/ha. Grain yield showed highly significant positive correlations with stem girth (r= 0.67) and number of ears (r=0.6), but significant negative correlation with anthesis-silking interval (r= -0.55). The dendrogram grouped 14 genotypes into four clusters. Cluster I incorporated the highest number (five) of genotypes, which also had highest cluster mean (average yield of ~10 mt/ha) for grain yield. Traits namely test weight, ear aspect, anthesis-silking interval, number of ears, and tassel branching had high genotypic and phenotypic coefficient of variations, and heritability along with high genetic advances, indicating that these traits can be considered for maize breeding program

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

Simple hydrogenic estimates for the exchange and correlation energies of atoms and atomic ions, with implications for density functional theory

Exact density functionals for the exchange and correlation energies are approximated in practical calculations for the ground-state electronic structure of a many-electron system. An important exact constraint for the construction of approximations is to recover the correct non-relativistic large-$Z$ expansions for the corresponding energies of neutral atoms with atomic number $Z$ and electron number $N=Z$, which are correct to leading order ($-0.221 Z^{5/3}$ and $-0.021 Z \ln Z$ respectively) even in the lowest-rung or local density approximation. We find that hydrogenic densities lead to $E_x(N,Z) \approx -0.354 N^{2/3} Z$ (as known before only for $Z \gg N \gg 1$) and $E_c \approx -0.02 N \ln N$. These asymptotic estimates are most correct for atomic ions with large $N$ and $Z \gg N$, but we find that they are qualitatively and semi-quantitatively correct even for small $N$ and for $N \approx Z$. The large-$N$ asymptotic behavior of the energy is pre-figured in small-$N$ atoms and atomic ions, supporting the argument that widely-predictive approximate density functionals should be designed to recover the correct asymptotics. It is shown that the exact Kohn-Sham correlation energy, when calculated from the pure ground-state wavefunction, should have no contribution proportional to $Z$ in the $Z\to \infty$ limit for any fixed $N$.Comment: This work has been accepted for publication at the Journal of Chemical Physics. Revisions: new Appendix A (former Appendix A is now Appendix B) discussing exact Kohn-Sham perturbation series for Ec. Added material discussing the Becke 1988 functional. More discussion of non-empirical functionals' recovery of the asymptotic series, and their accuracy in predicting atomic/molecular energie

Monitoring Climate Impacts on Annual Forage Production across U.S. Semi-Arid Grasslands

The ecosystem performance approach, used in a previously published case study focusing on the Nebraska Sandhills, proved to minimize impacts of non-climatic factors (e.g., overgrazing, fire, pests) on the remotely-sensed signal of seasonal vegetation greenness resulting in a better attribution of its changes to climate variability. The current study validates the applicability of this approach for assessment of seasonal and interannual climate impacts on forage production in the western United States semi-arid grasslands. Using a piecewise regression tree model, we developed the Expected Ecosystem Performance (EEP), a proxy for annual forage production that reflects climatic influences while minimizing impacts of management and disturbances. The EEP model establishes relations between seasonal climate, site-specific growth potential, and long-term growth variability to capture changes in the growing season greenness measured via a time-integrated Normalized Difference Vegetation Index (NDVI) observed using a Moderate Resolution Imaging Spectroradiometer (MODIS). The resulting 19 years of EEP were converted to expected biomass (EB, kg ha-1 year-1) using a newly-developed relation with the Soil Survey Geographic Database range production data (R2= 0.7). Results were compared to ground-observed biomass datasets collected by the U.S. Department of Agriculture and University of Nebraska-Lincoln (R2 = 0.67). This study illustrated that this approach is transferable to other semi-arid and arid grasslands and can be used for creating timely, post-season forage production assessments. When combined with seasonal climate predictions, it can provide within-season estimates of annual forage production that can serve as a basis for more informed adaptive decision making by livestock producers and land managers

Intensification differentially affects the delivery of multiple ecosystem services in subtropical and temperate grasslands

Intensification, the process of intensifying land management to enhance agricultural goods, results in “intensive” pastures that are planted with productive grasses and fertilized. These intensive pastures provide essential ecosystem services, including forage production for livestock. Understanding the synergies and tradeoffs of pasture intensification on the delivery of services across climatic regions is crucial to shape policies and incentives for better management of natural resources. Here, we investigated how grassland intensification affects key components of provisioning (forage productivity and quality), supporting (plant diversity) and regulating services (CO2 and CH4 fluxes) by comparing these services between intensive versus extensive pastures in subtropical and temperate pastures in the USDA Long-term Agroecosystem Research (LTAR) Network sites in Florida and Oklahoma, USA over multiple years. Our results suggest that grassland intensification led to a decrease in measured supporting and regulating services, but increased forage productivity in temperate pastures and forage digestibility in subtropical pastures. Intensification decreased the net CO2 sink of subtropical pastures while it did not affect the sink capacity of temperate pastures; and it also increased environmental CH4 emissions from subtropical pastures and reduced CH4 uptake in temperate pastures. Intensification enhanced the global warming potential associated with C fluxes of pastures in both ecoregions. Our study demonstrates that comparisons of agroecosystems in contrasting ecoregions can reveal important drivers of ecosystem services and general or region-specific opportunities and solutions to maintaining agricultural production and reducing environmental footprints. Further LTAR network-scale comparisons of multiple ecosystem services across croplands and grazinglands intensively vs extensively managed are warranted to inform the sustainable intensification of agriculture within US and beyond. Our results highlight that achieving both food security and environmental stewardship will involve the conservation of less intensively managed pastures while adopting sustainable strategies in intensively managed pastures

Dormant Season Vegetation Phenology and Eddy Fluxes in Native Tallgrass Prairies of the U.S. Southern Plains

Carbon dioxide (CO2) fluxes and evapotranspiration (ET) during the non-growing season can contribute significantly to the annual carbon and water budgets of agroecosystems. Comparative studies of vegetation phenology and the dynamics of CO2 fluxes and ET during the dormant season of native tallgrass prairies from different landscape positions under the same climatic regime are scarce. Thus, this study compared the dynamics of satellite-derived vegetation phenology (as captured by the enhanced vegetation index (EVI) and the normalized difference vegetation index (NDVI)) and eddy covariance (EC)-measured CO2 fluxes and ET in six differently managed native tallgrass prairie pastures during dormant seasons (November through March). During December–February, vegetation phenology (EVI and NDVI) and the dynamics of eddy fluxes were comparable across all pastures in most years. Large discrepancies in fluxes were observed during March (the time of the initiation of growth of dominant warm-season grasses) across years and pastures due to the influence of weather conditions and management practices. The results illustrated the interactive effects between prescribed spring burns and rainfall on vegetation phenology (i.e., positive and negative impacts of prescribed spring burns under non-drought and drought conditions, respectively). The EVI better tracked the phenology of tallgrass prairie during the dormant season than did NDVI. Similar EVI and NDVI values for the periods when flux magnitudes were different among pastures and years, most likely due to the satellite sensors’ inability to fully observe the presence of some cool-season C3 species under residues, necessitated a multi-level validation approach of using ground-truth observations of species composition, EC measurements, PhenoCam (digital) images, and finer-resolution satellite data to further validate the vegetation phenology derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) during dormant seasons. This study provides novel insights into the dynamics of vegetation phenology, CO2 fluxes, and ET of tallgrass prairie during the dormant season in the U.S. Southern Great Plains

Estimation and analysis of gross primary production of soybean under various management practices and drought conditions

Gross primary production (GPP) of croplands may be used to quantify crop productivity and evaluate a range of management practices. Eddy flux data from three soybean (Glycine max L.) fields under different management practices (no-till vs. till; rainfed vs. irrigated) and Moderate Resolution Imaging Spectroradiometer (MODIS) derived vegetation indices (VIs) were used to test the capabilities of remotely sensed VIs and soybean phenology to estimate the seasonal dynamics of carbon fluxes. The modeled GPP (GPPVPM) using vegetation photosynthesis model (VPM) was compared with the GPP (GPPEC) estimated from eddy covariance measurements. The VIs tracked soybean phenology well and delineated the growing season length (GSL), which was closely related to carbon uptake period (CUP, R2 = 0.84), seasonal sums of net ecosystem CO2 exchange (NEE, R2 = 0.78), and GPPEC (R2 = 0.54). Land surface water index (LSWI) tracked drought-impacted vegetation well, as the LSWI values were positive during non-drought periods and negative during severe droughts within the soybean growing season. On a seasonal scale, NEE of the soybean sites ranged from –37 to –264 gCm–2. The result suggests that rainfed soybean fields needed about 450–500 mm of well-distributed seasonal rainfall to maximize the net carbon sink. During non-drought conditions, VPM accurately estimated seasonal dynamics and interannual variation of GPP of soybean under different management practices. However, some large discrepancies between GPPVPM and GPPEC were observed under drought conditions as the VI did not reflect the corresponding decrease in GPPEC. Diurnal GPPEC dynamics showed a bimodal distribution with a pronounced midday depression at the period of higher water vapor pressure deficit (\u3e1.2 kPa). A modified Wscalar based on LSWI to account for the water stress in VPM helped quantify the reduction in GPP during severe drought and the model’s performance improved substantially. In conclusion, this study demonstrates the potential of integrating vegetation activity through satellite remote sensing with ground-based flux and climate data for a better understanding and upscaling of carbon fluxes of soybean croplands

Remote Sensing of Evapotranspiration (ET)

Evapotranspiration (ET) is a critical component of the water and energy balances, and the number of remote sensing-based ET products and estimation methods has increased in recent years. Various aspects of remote sensing of ET are reported in the 11 papers published in this book. The major research areas covered by this book include inter-comparison and performance evaluation of widely used one- and two-source energy balance models, a new dual-source model (Soil Plant Atmosphere and Remote Sensing Evapotranspiration, SPARSE), and a process-based model (ETMonitor); assessment of multi-source (e.g., remote sensing, reanalysis, and land surface model) ET products; development or improvement of data fusion frameworks to predict continuous daily ET at a high spatial resolution (field-scale or 30 m) by fusing the advanced spaceborne thermal emission reflectance radiometer (ASTER), the moderate resolution imaging spectroradiometer (MODIS), and Landsat data; and investigating uncertainties in ET estimates using an ET ensemble composed of several land surface models and diagnostic datasets. The effects of the differences between ET products on water resources and ecosystem management were also investigated. More accurate ET estimates and improved understanding of remotely sensed ET products are crucial for maximizing crop productivity while minimizing water losses and management costs