Remote sensing (RS) of plant canopies permits non-intrusive, high-throughput monitoring of plant physiological characteristics. This study compared three RS approaches using a low flying UAV (unmanned aerial vehicle), with that of proximal sensing, and satellite-based imagery. Two physiological traits were considered, canopy temperature (CT) and a vegetation index (NDVI), to determine the most viable approaches for large scale crop genetic improvement. The UAV-based platform achieves plot-level resolution while measuring several hundred plots in one mission via high-resolution thermal and multispectral imagery measured at altitudes of 30–100 m. The satellite measures multispectral imagery from an altitude of 770 km. Information was compared with proximal measurements using IR thermometers and an NDVI sensor at a distance of 0.5–1 m above plots. For robust comparisons, CT and NDVI were assessed on panels of elite cultivars under irrigated and drought conditions, in different thermal regimes, and on un-adapted genetic resources under water deficit. Correlations between airborne data and yield/biomass at maturity were generally higher than equivalent proximal correlations. NDVI was derived from high-resolution satellite imagery for only larger sized plots (8.5 × 2.4 m) due to restricted pixel density. Results support use of UAV-based RS techniques for high-throughput phenotyping for both precision and efficiency.

This study examines genomic prediction within 8416 Mexican landrace accessions and 2403 Iranian landrace accessions stored in gene banks. The Mexican and Iranian collections were evaluated in separate field trials, including an optimum environment for several traits, and in two separate environments (drought, D and heat, H) for the highly heritable traits, days to heading (DTH), and days to maturity (DTM). Analyses accounting and not accounting for population structure were performed. Genomic prediction models include genotype × environment interaction (G × E). Two alternative prediction strategies were studied: (1) random cross-validation of the data in 20% training (TRN) and 80% testing (TST) (TRN20-TST80) sets, and (2) two types of core sets, “diversity” and “prediction”, including 10% and 20%, respectively, of the total collections. Accounting for population structure decreased prediction accuracy by 15–20% as compared to prediction accuracy obtained when not accounting for population structure. Accounting for population structure gave prediction accuracies for traits evaluated in one environment for TRN20-TST80 that ranged from 0.407 to 0.677 for Mexican landraces, and from 0.166 to 0.662 for Iranian landraces. Prediction accuracy of the 20% diversity core set was similar to accuracies obtained for TRN20-TST80, ranging from 0.412 to 0.654 for Mexican landraces, and from 0.182 to 0.647 for Iranian landraces. The predictive core set gave similar prediction accuracy as the diversity core set for Mexican collections, but slightly lower for Iranian collections. Prediction accuracy when incorporating G × E for DTH and DTM for Mexican landraces for TRN20-TST80 was around 0.60, which is greater than without the G × E term. For Iranian landraces, accuracies were 0.55 for the G × E model with TRN20-TST80. Results show promising prediction accuracies for potential use in germplasm enhancement and rapid introgression of exotic germplasm into elite materials.

[1] Current good practice guidelines for national greenhouse gas inventories requires that seasonal variation in emission factors from savanna fires be considered when compiling national accounts. African studies concluded that the emission factor for methane decreases during the dry season principally due to curing of the fuels. However, available data from Australian tropical savannas shows no effect of seasonality on emission factors, consistent with observations that the fine fuels appear to cure fully soon after the start of the fire season. To test whether the seasonality in greenhouse gas emission factors reported for Africa also occurs in Australia, methane and nitrous oxide emission factors were measured in early and in late dry season fires in Western Arnhem Land, a region typical of much of the northern Australia savanna zone. We found no significant seasonality in methane emission factors, but there was substantial variation in emission factors associated with inter-fire differences in vegetation and fuel. This variation could be explained almost completely by combustion efficiency. Nitrous oxide emission factors were not related to combustion efficiency but showed some variation across vegetation and fuel size class. Both methane and nitrous oxide emission factors were consistent with previous work in northern Australia and with some published values from Africa. The absence of a significant seasonal trend in emission factors indicates that savanna fire emissions in northern Australia can be managed by strategic prescribed burning.

Climate change and slow yield gains pose a major threat to global wheat production. Underutilized genetic resources including landraces and wild relatives are key elements for developing high-yielding and climate-resilient wheat varieties. Landraces introduced into Mexico from Europe, also known as Creole wheats, are adapted to a wide range of climatic regimes and represent a unique genetic resource. Eight thousand four hundred and sixteen wheat landraces representing all dimensions of Mexico were characterized through genotyping-by-sequencing technology. Results revealed sub-groups adapted to specific environments of Mexico. Broadly, accessions from north and south of Mexico showed considerable genetic differentiation. However, a large percentage of landrace accessions were genetically very close, although belonged to different regions most likely due to the recent (nearly five centuries before) introduction of wheat in Mexico. Some of the groups adapted to extreme environments and accumulated high number of rare alleles. Core reference sets were assembled simultaneously using multiple variables, capturing 89% of the rare alleles present in the complete set. Genetic information about Mexican wheat landraces and core reference set can be effectively utilized in next generation wheat varietal improvement.

Abstract. When compared to established point-sampling methods, Open-Path Fourier Transform Infrared (OP-FTIR) spectroscopy can provide path-integrated concentrations of multiple gases simultaneously, in situ and near-continuously. The trace gas pathlength amounts can be retrieved from the measured IR spectra using a forward model coupled to a non-linear least squares fitting procedure, without requiring "background" spectral measurements unaffected by the gases of interest. However, few studies have investigated the accuracy of such retrievals for CO 2 , CH 4 and CO, particularly across broad concentration ranges covering those characteristic of ambient to highly polluted air (e.g. from biomass burning or industrial plumes). Here we perform such an assessment using data collected by a field-portable FTIR spectrometer. The FTIR was positioned to view a fixed IR source placed at the other end of an IR-transparent cell filled with the gases of interest, whose target concentrations were varied by more than two orders of magnitude. Retrievals made using the model are complicated by absorption line pressure broadening, the effects of temperature on absorption band shape, and by convolution of the gas absorption lines and the instrument line shape (ILS). Despite this, with careful model parameterisation (i.e. the optimum wavenumber range, ILS, and assumed gas temperature and pressure for the retrieval), concentrations for all target gases were able to be retrieved to within 5%. Sensitivity to the aforementioned model inputs was also investigated. CO retrievals were shown to be Correspondence to: T. E. L. Smith (thomas.smith@kcl.ac.uk) most sensitive to the ILS (a function of the assumed instrument field-of-view), which is due to the narrow nature of CO absorption lines and their consequent sensitivity to convolution with the ILS. Conversely, CO 2 retrievals were most sensitive to assumed atmospheric parameters, particularly gas temperature. Our findings provide confidence that FTIRderived trace gas retrievals of CO 2 , CH 4 and CO based on modeling can yield results with high accuracies, even over very large (many order of magnitude) concentration ranges that can prove difficult to retrieve via standard classical least squares (CLS) techniques. With the methods employed here, we suggest that errors in the retrieved trace gas concentrations should remain well below 10%, even with the uncertainties in atmospheric pressure and temperature that might arise when studying plumes in more difficult field situations (e.g. at uncertain altitudes or temperatures).