ALBERT

Description: Yellow-flowered alfalfa (Medicago sativa subsp. falcata) (also known as sickle medic) has been the cornerstone for breeding alfalfa for dual grazing and hay production in the semiarid regions of the northern Great Plains in the US and Canada. Most, if not all, of the cultivars developed for the northern Great Plains during the 20th century, had parentage tracing back to introductions by Niels Ebbesen Hansen that were obtained from expeditions to Russia, primarily the province of Siberia, on behalf of the United States Department of Agriculture during the early 1900s. The M. falcata genome contains alleles for high levels of drought-tolerance, winter hardiness, and tolerance to grazing, but is generally deficient for commercial seed production traits, such as non-shatter, compared with common alfalfa (M. sativa). A naturalized population, tracing to USDA plant introductions to Perkins County South Dakota by N.E. Hansen in early 1900, and subsequently, facilitated by the determined seed increase and interseeding of a population by a local rancher, Norman ‘Bud’ Smith, has shown highly desirable in situ characteristics for improving rangelands in the northern Great Plains. This includes adequate seed production to build a seed bank in the soil for natural seedling recruitment and population maintenance/expansion and support the production of a commercial seed source. This review documents the seminal events in the development of cultivars to date and describes novel germplasm with potential for new cultivars in the future.

Description: The increasing drought severities and consequent devastating impacts on society over the Indian semi-arid regions demand better drought monitoring and early warning systems. Operational agricultural drought assessment methods in India mainly depend on a single input parameter such as precipitation and are based on a sparsely located in-situ measurements, which limits monitoring precision. The overarching objective of this study is to address this need through the development of an integrated agro-climatological drought monitoring approach, i.e., combined drought indicator for Marathwada (CDI_M), situated in the central part of Maharashtra, India. In this study, satellite and model-based input parameters (i.e., standardized precipitation index (SPI-3), land surface temperature (LST), soil moisture (SM), and normalized difference vegetation index (NDVI)) were analyzed at a monthly scale from 2001 to 2018. Two quantitative methods were tested to combine the input parameters for developing the CDI_M. These methods included an expert judgment-based weight of each parameter (Method-I) and principle component analysis (PCA)-based weighting approach (Method-II). Secondary data for major types of crop yields in Marathwada were utilized to assess the CDI_M results for the study period. CDI_M maps depict moderate to extreme drought cases in the historic drought years of 2002, 2009, and 2015–2016. This study found a significant increase in drought intensities (p ≤ 0.05) and drought frequency over the years 2001–2018, especially in the Latur, Jalna, and Parbhani districts. In comparison to Method-I (r ≥ 0.4), PCA-based (Method-II) CDI_M showed a higher correlation (r ≥ 0.60) with crop yields in both harvesting seasons (Kharif and Rabi). In particular, crop yields during the drier years showed a greater association (r 〉 6.5) with CDI_M over Marathwada. Hence, the present study illustrated the effectiveness of CDI_M to monitor agricultural drought in India and provide improved information to support agricultural drought management practices.

Description: Snowmelt from mountain forests is critically important for water resources and hydropower generation. More than 75% of surface water supply originates as snowmelt in mountainous regions, such as the western U.S. Remote sensing has the potential to measure snowpack in these areas accurately. In this research, we combine light detection and ranging (lidar) from crewed aircraft (currently, the most reliable way of measuring snow depth in mountain forests) and structure from motion (SfM) remotely piloted aircraft systems (RPAS) for cost-effective multi-temporal monitoring of snowpack in mountain forests. In sparsely forested areas, both technologies give similar snow depth maps, with a comparable agreement with ground-based snow depth observations (RMSE ~10 cm). In densely forested areas, airborne lidar is better able to represent snow depth than RPAS-SfM (RMSE ~10 cm vs ~10–20 cm). In addition, we find the relationship between RPAS-SfM and previous lidar snow depth data can be used to estimate snow depth conditions outside of relatively small RPAS-SfM monitoring plots, with RMSE’s between these observed and estimated snow depths on the order of 10–15 cm for the larger lidar coverages. This suggests that when a single airborne lidar snow survey exists, RPAS-SfM may provide useful multi-temporal snow monitoring that can estimate basin-scale snowpack, at a much lower cost than multiple airborne lidar surveys. Doing so requires a pre-existing mid-winter or peak-snowpack airborne lidar snow survey, and subsequent well-designed paired SfM and field snow surveys that accurately capture substantial snow depth variability.

Description: Innovative breeds of sugar cane yield up to 2.5 times as much organic matter as conventional breeds, resulting in a great potential for biogas production. The use of biogas production as a complementary solution to conventional and second-generation ethanol production in Brazil may increase the energy produced per hectare in the sugarcane sector. Herein, it was demonstrated that through ensiling, energy cane can be conserved for six months; the stored cane can then be fed into a continuous biogas process. This approach is necessary to achieve year-round biogas production at an industrial scale. Batch tests revealed specific biogas potentials between 400 and 600 LN/kgVS for both the ensiled and non-ensiled energy cane, and the specific biogas potential of a continuous biogas process fed with ensiled energy cane was in the same range. Peak biogas losses through ensiling of up to 27% after six months were observed. Finally, compared with second-generation ethanol production using energy cane, the results indicated that biogas production from energy cane may lead to higher energy yields per hectare, with an average energy yield of up to 162 MWh/ha. Finally, the Farm2CBG concept is introduced, showing an approach for decentralized biogas production.

Description: Recent applications of Landsat 8 Operational Land Imager (L8/OLI) and Sentinel-2 MultiSpectral Instrument (S2/MSI) data for acquiring information about land use and land cover (LULC) provide a new perspective in remote sensing data analysis. Jointly, these sources permit researchers to improve operational classification and change detection, guiding better reasoning about landscape and intrinsic processes, as deforestation and agricultural expansion. However, the results of their applications have not yet been synthesized in order to provide coherent guidance on the effect of their applications in different classification processes, as well as to identify promising approaches and issues which affect classification performance. In this systematic review, we present trends, potentialities, challenges, actual gaps, and future possibilities for the use of L8/OLI and S2/MSI for LULC mapping and change detection. In particular, we highlight the possibility of using medium-resolution (Landsat-like, 10–30 m) time series and multispectral optical data provided by the harmonization between these sensors and data cube architectures for analysis-ready data that are permeated by publicizations, open data policies, and open science principles. We also reinforce the potential for exploring more spectral bands combinations, especially by using the three Red-edge and the two Near Infrared and Shortwave Infrared bands of S2/MSI, to calculate vegetation indices more sensitive to phenological variations that were less frequently applied for a long time, but have turned on since the S2/MSI mission. Summarizing peer-reviewed papers can guide the scientific community to the use of L8/OLI and S2/MSI data, which enable detailed knowledge on LULC mapping and change detection in different landscapes, especially in agricultural and natural vegetation scenarios.

Description: This paper reviews the scientific motivation and challenges, development, and use of underwater robotic vehicles designed for use in ice-covered waters, with special attention paid to the navigation systems employed for under-ice deployments. Scientific needs for routine access under fixed and moving ice by underwater robotic vehicles are reviewed in the contexts of geology and geophysics, biology, sea ice and climate, ice shelves, and seafloor mapping. The challenges of under-ice vehicle design and navigation are summarized. The paper reviews all known under-ice robotic vehicles and their associated navigation systems, categorizing them by vehicle type (tethered, untethered, hybrid, and glider) and by the type of ice they were designed for (fixed glacial or sea ice and moving sea ice).

Description: Fast and accurate quantification of the available pasture biomass is essential to support grazing management decisions in intensively managed fields. The increasing temporal and spatial resolutions offered by the new generation of orbital platforms, such as Planet CubeSat satellites, have improved the capability of monitoring pasture biomass using remotely sensed data. Here, we assessed the feasibility of using spectral and textural information derived from PlanetScope imagery for estimating pasture aboveground biomass (AGB) and canopy height (CH) in intensively managed fields and the potential for enhanced accuracy by applying the extreme gradient boosting (XGBoost) algorithm. Our results demonstrated that the texture measures enhanced AGB and CH estimations compared to the performance obtained using only spectral bands or vegetation indices. The best results were found by employing the XGBoost models based only on texture measures. These models achieved moderately high accuracy to predict pasture AGB and CH, explaining 65% and 89% of AGB (root mean square error (RMSE) = 26.52%) and CH (RMSE = 20.94%) variability, respectively. This study demonstrated the potential of using texture measures to improve the prediction accuracy of AGB and CH models based on high spatiotemporal resolution PlanetScope data in intensively managed mixed pastures.

Description: Preparing agricultural producers to cope with volatile weather changes, specifically drought, requires a better understanding of forage water-use efficiency (WUE) potentials. Options to improve farm resiliency to drought may include the use of C4 annual and perennial forages, which have greater production efficiency during drought than commonly used C3 forages. Our objective was to measure WUE through real-time gas exchange measurements of photosynthesis and transpiration in (1) a greenhouse study and (2) under field-grazing conditions. Growth parameters, instantaneous water use efficiency (iWUE), and mass-based WUE (mWUE) data were collected under greenhouse conditions in Study 1 for the following species: crabgrass (Digitaria sanguinalis cv. ‘Red River’), switchgrass (Panicum virgatum cv. ‘Alamo’), big bluestem (Andropogon gerardii cv. ‘OZ-70’), indiangrass (Sorghastum nutans cv. ‘Rumsey’), eastern gamagrass (Tripsacum dactyloides cv. ‘Pete’), bermudagrass (Cynodon dactylon cv. ‘Vaughn’s #1’), sorghum-sudangrass (Sorghum bicolor (L.) × Sorghum sudanese (P.) cv. ‘Greengrazer’), and tall fescue (Schedonorus arundinaceus (Schreb.) Dumort). Study 2 occurred from 2014 to 2016, and evaluated iWUE of crabgrass, switchgrass, bermudagrass, eastern gamagrass, and a big bluestem/indiangrass mix under field conditions. Overall, in situ iWUE of crabgrass, switchgrass, eastern gamagrass, and bermudagrass did not differ, while iWUE of the big bluestem/indiangrass was less than switchgrass and crabgrass, an advantage for these species if the standardized precipitation index drops below zero. Bermudagrass, switchgrass, sorghum-sudangrass, pearl millet, and indiangrass had comparable mWUE values under greenhouse-simulated drought. These results will aid in the development of forage species recommendations for mitigating drought and improving resiliency.

Description: Establishing linkages between light detection and ranging (lidar) data, produced from interrogating forest canopies, to the highly complex forest structures, composition, and traits that such forests contain, remains an extremely difficult problem. Radiative transfer models have been developed to help solve this problem and test new sensor platforms in a virtual environment. Many forest canopy studies include the major assumption of isotropic (Lambertian) reflecting and transmitting leaves or non-transmitting leaves. Here, we study when these assumptions may be valid and evaluate their associated impacts/effects on the lidar waveform, as well as its dependence on wavelength, lidar footprint, view angle, and leaf angle distribution (LAD), by using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) remote sensing radiative transfer simulation model. The largest effects of Lambertian assumptions on the waveform are observed at visible wavelengths, small footprints, and oblique interrogation angles relative to the mean leaf angle. For example, a 77% increase in return signal was observed with a configuration of a 550 nm wavelength, 10 cm footprint, and 45° interrogation angle to planophile leaves. These effects are attributed to (i) the bidirectional scattering distribution function (BSDF) becoming almost purely specular in the visible, (ii) small footprints having fewer leaf angles to integrate over, and (iii) oblique angles causing diminished backscatter due to forward scattering. Non-transmitting leaf assumptions have the greatest error for large footprints at near-infrared (NIR) wavelengths. Regardless of leaf angle distribution, all simulations with non-transmitting leaves with a 5 m footprint and 1064 nm wavelength saw around a 15% reduction in return signal. We attribute the signal reduction to the increased multiscatter contribution for larger fields of view, and increased transmission at NIR wavelengths. Armed with the knowledge from this study, researchers will be able to select appropriate sensor configurations to account for or limit BSDF effects in forest lidar data.

Description: Juvenile and vegetative adult shoot apical meristems (SAM) are actively involved in acquisition of flowering competence, while the embryonic SAM is regarded as “responsible” only for germination. Comparative analyses of imbibed and stratified seeds of garlic Allium sativum show that only stratified seedlings produced bulbs and flower stems at the end of the season. Since the seed morphology of stratified and non-stratified seeds prior to sowing was similar, the differences are attributed to the molecular alterations in the embryonic SAM. Functional annotation analysis of 3000 differentially expressed genes suggests that seed imbibition reactivates the embryonic cell cycle, initiates the metabolism, and primes garlic seed germination. Stratification enhances DNA modifications, biosynthesis, cellular transport, and tissue development. Similar to vernalization of the vegetative buds, stratification of the embryonic SAM resulted in altered expression of meristem-identity and flowering homologs. Phase transitions from seed germination to flowering and bulbing in A. sativum are tightly connected, and possibly associated with downregulation of specific flowering repressor(s). The embryonic SAM plays an important role not only in seed germination, but in the entire plant life cycle, providing the foundation for the genetic regulation of major functional shifts in metabolism and development.