Changes in spatiotemporal protein and amino acid gradients in wheat caryopsis after N-topdressing.

Wheat grain nitrogen content displays large variations within different pearling fractions of grains because of radial gradients in the protein content. We identified how spatiotemporal mechanisms regulate this. The protein gradients emerged clearly at 19 days after anthesis, with the highest N content in aleurone and seed coat, followed by outer endosperm, whereas the lowest was in middle and inner endosperm. Laser microdissection, qRT-PCR and LC-MS were used to dissect tissue from aleurone, outer endosperm, middle endosperm, inner endosperm and transfer cells, measure gene expression and levels of free and protein-bound amino acids, respectively. The results showed that different FAA transportation pathways worked in parallel during grain filling stage while the grain protein gradient did not follow spatial expression of storage proteins. Additionally, two nitrogen (N) topdressing timings were conducted, either at the emergence of top third leaf (standard timing) or top first leaf (delayed timing), finding that delayed N topdressing enhanced both amino acids supply and protein synthesis capacity. The results provide insight into protein synthesis and amino acid transport pathways in endosperm and suggest targets for the enhancement of specialty pearled wheat with higher quality.

Robust Species Distribution Mapping of Crop Mixtures Using Color Images and Convolutional Neural Networks.

Crop mixtures are often beneficial in crop rotations to enhance resource utilization and yield stability. While targeted management, dependent on the local species composition, has the potential to increase the crop value, it comes at a higher expense in terms of field surveys. As fine-grained species distribution mapping of within-field variation is typically unfeasible, the potential of targeted management remains an open research area. In this work, we propose a new method for determining the biomass species composition from high resolution color images using a DeepLabv3+ based convolutional neural network. Data collection has been performed at four separate experimental plot trial sites over three growing seasons. The method is thoroughly evaluated by predicting the biomass composition of different grass clover mixtures using only an image of the canopy. With a relative biomass clover content prediction of R2 = 0.91, we present new state-of-the-art results across the largely varying sites. Combining the algorithm with an all terrain vehicle (ATV)-mounted image acquisition system, we demonstrate a feasible method for robust coverage and species distribution mapping of 225 ha of mixed crops at a median capacity of 17 ha per hour at 173 images per hectare.

Expression of starch-binding factor CBM20 in barley plastids controls the number of starch granules and the level of CO2 fixation.

The biosynthesis of starch granules in plant plastids is coordinated by the orchestrated action of transferases, hydrolases, and dikinases. These enzymes either contain starch-binding domain(s) themselves, or are dependent on direct interactions with co-factors containing starch-binding domains. As a means to competitively interfere with existing starch-protein interactions, we expressed the protein module Carbohydrate-Binding Motif 20 (CBM20), which has a very high affinity for starch, ectopically in barley plastids. This interference resulted in an increase in the number of starch granules in chloroplasts and in formation of compound starch granules in grain amyloplasts, which is unusual for barley. More importantly, we observed a photosystem-independent inhibition of CO2 fixation, with a subsequent reduced growth rate and lower accumulation of carbohydrates with effects throughout the metabolome, including lower accumulation of transient leaf starch. Our results demonstrate the importance of endogenous starch-protein interactions for controlling starch granule morphology and number, and plant growth, as substantiated by a metabolic link between starch-protein interactions and control of CO2 fixation in chloroplasts.

Estimation of the Botanical Composition of Clover-Grass Leys from RGB Images Using Data Simulation and Fully Convolutional Neural Networks.

Optimal fertilization of clover-grass fields relies on knowledge of the clover and grass fractions. This study shows how knowledge can be obtained by analyzing images collected in fields automatically. A fully convolutional neural network was trained to create a pixel-wise classification of clover, grass, and weeds in red, green, and blue (RGB) images of clover-grass mixtures. The estimated clover fractions of the dry matter from the images were found to be highly correlated with the real clover fractions of the dry matter, making this a cheap and non-destructive way of monitoring clover-grass fields. The network was trained solely on simulated top-down images of clover-grass fields. This enables the network to distinguish clover, grass, and weed pixels in real images. The use of simulated images for training reduces the manual labor to a few hours, as compared to more than 3000 h when all the real images are annotated for training. The network was tested on images with varied clover/grass ratios and achieved an overall pixel classification accuracy of 83.4%, while estimating the dry matter clover fraction with a standard deviation of 7.8%.

Designing and Testing a UAV Mapping System for Agricultural Field Surveying.

A Light Detection and Ranging (LiDAR) sensor mounted on an Unmanned Aerial Vehicle (UAV) can map the overflown environment in point clouds. Mapped canopy heights allow for the estimation of crop biomass in agriculture. The work presented in this paper contributes to sensory UAV setup design for mapping and textual analysis of agricultural fields. LiDAR data are combined with data from Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) sensors to conduct environment mapping for point clouds. The proposed method facilitates LiDAR recordings in an experimental winter wheat field. Crop height estimates ranging from 0.35-0.58 m are correlated to the applied nitrogen treatments of 0-300 kg N ha . The LiDAR point clouds are recorded, mapped, and analysed using the functionalities of the Robot Operating System (ROS) and the Point Cloud Library (PCL). Crop volume estimation is based on a voxel grid with a spatial resolution of 0.04 × 0.04 × 0.001 m. Two different flight patterns are evaluated at an altitude of 6 m to determine the impacts of the mapped LiDAR measurements on crop volume estimations.

The effect of harvest time, dry matter content and mechanical pretreatments on anaerobic digestion and enzymatic hydrolysis of miscanthus.

Miscanthus x giganteus was harvested as both green and mature biomass and the dry matter content of the driest harvest was artificially decreased by adding water in two subsamples, giving a total of five dry matter contents. All five biomass types were mechanically pretreated by roller-milling, extrusion or grinding and accumulated methane production and enzymatically-accessible sugars were measured. Accumulated methane production was studied using sigmoid curves that allowed comparison among the treatments of the rate of the methane production and ultimate methane yield. The green biomass gave the highest methane yield and highest levels of enzymatically-accessible cellulose. The driest biomass gave the best effect from extrusion but with the highest energy consumption, whereas roller-milling was most efficient on wet biomass. The addition of water to the last harvest improved the effect of roller-milling and equalled extrusion of the samples in efficiency.

Correlation of Deoxynivalenol Accumulation in Fusarium-Infected Winter and Spring Wheat Cultivars with Secondary Metabolites at Different Growth Stages.

Fusarium infection in wheat causes Fusarium head blight, resulting in yield losses and contamination of grains with trichothecenes. Some plant secondary metabolites inhibit accumulation of trichothecenes. Eighteen Fusarium infected wheat cultivars were harvested at five time points and analyzed for the trichothecene deoxynivalenol (DON) and 38 wheat secondary metabolites (benzoxazinoids, phenolic acids, carotenoids, and flavonoids). Multivariate analysis showed that harvest time strongly impacted the content of secondary metabolites, more distinctly for winter wheat than spring wheat. The benzoxazinoid 2-ß-glucopyranoside-2,4-dihydroxy-7-methoxy-1,4-benzoxazin-3-one (DIMBOA-glc), α-tocopherol, and the flavonoids homoorientin and orientin were identified as potential inhibitors of DON accumulation. Several phenolic acids, lutein and ß-carotene also affected DON accumulation, but the effect varied for the two wheat types. The results could form a basis for choosing wheat cultivars using metabolite profiling as a marker for selecting wheat cultivars with improved resistance against Fusarium head blight and accumulation of trichothecene toxins in wheat heads.

The Use of Image-Spectroscopy Technology as a Diagnostic Method for Seed Health Testing and Variety Identification.

Application of rapid and time-efficient health diagnostic and identification technology in the seed industry chain could accelerate required analysis, characteristic description and also ultimately availability of new desired varieties. The aim of the study was to evaluate the potential of multispectral imaging and single kernel near-infrared spectroscopy (SKNIR) for determination of seed health and variety separation of winter wheat (Triticum aestivum L.) and winter triticale (Triticosecale Wittm. & Camus). The analysis, carried out in autumn 2013 at AU-Flakkebjerg, Denmark, included nine winter triticale varieties and 27 wheat varieties provided by the Faculty of Agriculture and Life Sciences Maribor, Slovenia. Fusarium sp. and black point disease-infected parts of the seed surface could successfully be distinguished from uninfected parts with use of a multispectral imaging device (405-970 nm wavelengths). SKNIR was applied in this research to differentiate all 36 involved varieties based on spectral differences due to variation in the chemical composition. The study produced an interesting result of successful distinguishing between the infected and uninfected parts of the seed surface. Furthermore, the study was able to distinguish between varieties. Together these components could be used in further studies for the development of a sorting model by combining data from multispectral imaging and SKNIR for identifying disease(s) and varieties.

Profiling and Metabolism of Sterols in the Weaver Ant Genus Oecophylla.

Sterols are essential to insects because they are vital for many biochemical processes, nevertheless insects cannot synthesize sterols but have to acquire them through their diet. Studies of sterols in ants are sparse and here the sterols of the weaver ant genus Oecophylla are identified for the first time. The sterol profile and the dietary sterols provided to a laboratory Oecophylla longinoda colony were analyzed. Most sterols originated from the diet, except one, which was probably formed via dealkylation in the ants and two sterols of fungal origin, which likely originate from hitherto unidentified endosymbionts responsible for supplying these two compounds. The sterol profile of a wild Oecophylla smaragdina colony was also investigated. Remarkable qualitative similarities were established between the two species despite the differences in diet, species, and origin. This may reflect a common sterol need/aversion in the weaver ants. Additionally, each individual caste of both species displayed unique sterol profiles.

Grain protein concentration and harvestable protein under future climate conditions. A study of 108 spring barley accessions.

In the present study a set of 108 spring barley (H. vulgare L.) accessions were cultivated under predicted future levels of temperature and [CO2] as single factors and in combination (IPCC, AR5, RCP8.5). Across all genotypes, elevated [CO2] (700 ppm day/night) slightly decreased protein concentration by 5%, while elevated temperature (+5 °C day/night) substantially increased protein concentration by 29%. The combined treatment increased protein concentration across accessions by 8%. This was an increase less than predicted from strictly additive effects of the individual treatments. Despite the increase in grain protein concentration, the decrease in grain yield at combined elevated temperature and elevated [CO2] resulted in 23% less harvestable protein. There was variation in the response of the 108 accessions, which might be exploited to at least maintain if not increase harvestable grain protein under future climate change conditions.

Productivity and carbon footprint of perennial grass-forage legume intercropping strategies with high or low nitrogen fertilizer input.

A three-season field experiment was established and repeated twice with spring barley used as cover crop for different perennial grass-legume intercrops followed by a full year pasture cropping and winter wheat after sward incorporation. Two fertilization regimes were applied with plots fertilized with either a high or a low rate of mineral nitrogen (N) fertilizer. Life cycle assessment (LCA) was used to evaluate the carbon footprint (global warming potential) of the grassland management including measured nitrous oxide (N2O) emissions after sward incorporation. Without applying any mineral N fertilizer, the forage legume pure stand, especially red clover, was able to produce about 15 t above ground dry matter ha(-1) year(-1) saving around 325 kg mineral Nfertilizer ha(-1) compared to the cocksfoot and tall fescue grass treatments. The pure stand ryegrass yielded around 3t DM more than red clover in the high fertilizer treatment. Nitrous oxide emissions were highest in the treatments containing legumes. The LCA showed that the low input N systems had markedly lower carbon footprint values than crops from the high N input system with the pure stand legumes without N fertilization having the lowest carbon footprint. Thus, a reduction in N fertilizer application rates in the low input systems offsets increased N2O emissions after forage legume treatments compared to grass plots due to the N fertilizer production-related emissions. When including the subsequent wheat yield in the total aboveground production across the three-season rotation, the pure stand red clover without N application and pure stand ryegrass treatments with the highest N input equalled. The present study illustrate how leguminous biological nitrogen fixation (BNF) represents an important low impact renewable N source without reducing crop yields and thereby farmers earnings.

Use of multispectral imaging in varietal identification of tomato.

Multispectral imaging is an emerging non-destructive technology. In this work its potential for varietal discrimination and identification of tomato cultivars of Nepal was investigated. Two sample sets were used for the study, one with two parents and their crosses and other with eleven cultivars to study parents and offspring relationship and varietal identification respectively. Normalized canonical discriminant analysis (nCDA) and principal component analysis (PCA) were used to analyze and compare the results for parents and offspring study. Both the results showed clear discrimination of parents and offspring. nCDA was also used for pairwise discrimination of the eleven cultivars, which correctly discriminated upto 100% and only few pairs below 85%. Partial least square discriminant analysis (PLS-DA) was further used to classify all the cultivars. The model displayed an overall classification accuracy of 82%, which was further improved to 96% and 86% with stepwise PLS-DA models on high (seven) and poor (four) sensitivity cultivars, respectively. The stepwise PLS-DA models had satisfactory classification errors for cross-validation and prediction 7% and 7%, respectively. The results obtained provide an opportunity of using multispectral imaging technology as a primary tool in a scientific community for identification/discrimination of plant varieties in regard to genetic purity and plant variety protection/registration.

Viability prediction of Ricinus cummunis L. seeds using multispectral imaging.

The purpose of this study was to highlight the use of multispectral imaging in seed quality testing of castor seeds. Visually, 120 seeds were divided into three classes: yellow, grey and black seeds. Thereafter, images at 19 different wavelengths ranging from 375-970 nm were captured of all the seeds. Mean intensity for each single seed was extracted from the images, and a significant difference between the three colour classes was observed, with the best separation in the near-infrared wavelengths. A specified feature (RegionMSI mean) based on normalized canonical discriminant analysis, were employed and viable seeds were distinguished from dead seeds with 92% accuracy. The same model was tested on a validation set of seeds. These seeds were divided into two groups depending on germination ability, 241 were predicted as viable and expected to germinate and 59 were predicted as dead or non-germinated seeds. This validation of the model resulted in 96% correct classification of the seeds. The results illustrate how multispectral imaging technology can be employed for prediction of viable castor seeds, based on seed coat colour.

Prediction of biogas yield and its kinetics in reed canary grass using near infrared reflectance spectroscopy and chemometrics.

A rapid method is needed to assess biogas and methane yield potential of various kinds of substrate prior to anaerobic digestion. This study reports near infrared reflectance spectroscopy (NIRS) as a rapid alternative method to the conventional batch methods for prediction of specific biogas yield (SBY), specific methane yield (SMY) and kinetics of biogas yield (k-SBY) of reed canary grass (RCG) biomass. Dried and powdered RCG biomass with different level of maturity was used for biochemical composition analysis, batch assays and NIRS analysis. Calibration models were developed using partial least square (PLS) regression from NIRS spectra. The calibration models for SBY (R(2)=0.68, RPD=1.83) and k-SBY (R(2)=0.71, RPD=1.75) were better than the model for SMY (R(2)=0.53, RPD=1.49). Although the PLS model for SMY was less successful, the model performance was better compared to the models based on chemical composition.

Path and ridge regression analysis of seed yield and seed yield components of Russian wildrye (Psathyrostachys juncea Nevski) under field conditions.

The correlations among seed yield components, and their direct and indirect effects on the seed yield (Z) of Russina wildrye (Psathyrostachys juncea Nevski) were investigated. The seed yield components: fertile tillers m(-2) (Y(1)), spikelets per fertile tillers (Y(2)), florets per spikelet(-) (Y(3)), seed numbers per spikelet (Y(4)) and seed weight (Y(5)) were counted and the Z were determined in field experiments from 2003 to 2006 via big sample size. Y(1) was the most important seed yield component describing the Z and Y(2) was the least. The total direct effects of the Y(1), Y(3) and Y(5) to the Z were positive while Y(4) and Y(2) were weakly negative. The total effects (directs plus indirects) of the components were positively contributed to the Z by path analyses. The seed yield components Y(1), Y(2), Y(4) and Y(5) were significantly (P<0.001) correlated with the Z for 4 years totally, while in the individual years, Y(2) were not significant correlated with Y(3), Y(4) and Y(5) by Peason correlation analyses in the five components in the plant seed production. Therefore, selection for high seed yield through direct selection for large Y(1), Y(2) and Y(3) would be effective for breeding programs in grasses. Furthermore, it is the most important that, via ridge regression, a steady algorithm model between Z and the five yield components was founded, which can be closely estimated the seed yield via the components.