Sorghum Panicles Research Articles

Identification and Counting of Sorghum Panicles Using Artificial Intelligence Based Drone Field Phenotyping

Identification and Counting of Sorghum Panicles Using Artificial Intelligence Based Drone Field Phenotyping

Effect of supplementing wheat offal with varied graded levels of sugarcane scrapping on utilisation of sorghum panicle fed Red Sokoto buck Keyw

Four Red Sokoto bucks of averageweight 17. 40±2kg were fed sugarcane scrappingmeal in a complete diet to evaluate the nutrients intake, digestibility and nitrogen balance. In a 4×4 Latin Square Design four dietary treatments containing sugarcane scrapping at 0, 10, 20, and 30% levels of inclusion supplementing wheat offal in a complete diet were used The proximate composition of the sugarcane scrapping showed that the scrapping had, 10.01, 2.98%and 3114 Kcal/Kg of CF, CP andME respectively. The results of the trial showed that significant high dry matter intake, nutrient digestibility of some and the nitrogen balance values were recorded in the bucks fed 30%. It was concluded that DM intake, nutrient digestibility and nitrogen utilization of bucks can be enhanced by including 30% of sugar cane scrapping in their diets.

First report of Fusarium lacertarum causing Fusarium Head Blight on sorghum in North Carolina.

In August 2018, sorghum plants (Sorghum bicolor (L.) Moench) from research field plots in Wake County, North Carolina were observed with head blight symptoms including panicles with red lesions, visible mycelium, and necrosis. At the time of collection, all plants in research plots displayed symptoms of Fusarium head blight and panicles averaged 33% area affected by symptoms and signs. From these affected plants, samples (n = 5) were collected for further identification. Symptomatic grains were surface sterilized for one minute in 0.825% sodium hypochlorite solution and rinsed for one minute in sterile, deionized water. After drying on sterile paper towels, grains were plated onto water agar. Resulting fungal hyphal tips were then transferred to antibiotic-amended potato dextrose agar (PDA) and incubated at 25oC. Cultures were incubated for 3 to 5 days. Isolates had abundant white hyphae accompanied with peach-colored pigment production. Macroconidia with 5-6 septations were 23.47 ± 7.74 µm long and 3.47 ± 0.66 µm wide with foot-shaped basal cells, tapering to hooked apical cells. Chlamydospores were present in chains but microconidia were not present. Morphological species recognition (MSR) criteria tentatively identified the isolate as Fusarium lacertarum Subrahm., in the Fusarium incarnatum-equiseti species complex (FIESC) using characteristics described by Leslie and Summerell 2006. Molecular characterization using translation elongation factor 1α (TEF-1 α, primers EF1 and EF2 from O'Donnell et al. 1998), β tubulin (TUB2, primers T1 and T22 from O'Donnell and Cigelnik 1997), and ribosomal protein subunit II (RPB2, primers 5F2 and 11AR from Cerón-Bustamante et al. 2018) was conducted to confirm morphological identification. DNA from the hyphae of pure cultures was extracted using the DNeasy PowerSoil DNA extraction kit according to manufacturer's guidelines. DNA amplification conditions followed the protocols for each primer set (O'Donnell et al. 1998; O'Donnell and Cigelnik 1997; Cerón-Bustamante et al. 2018). BLASTn analysis of TEF-1α (Isolate Accession MT149915, 573bp) alignment had 99.8% identity to F. lacertarum (NCBI accession: JF740828), TUB2 (Isolate Accession MT149914, 1,183bp) alignment had 99.3% identity to F. equiseti (NCBI accession: KJ396338), and RPB2 (Isolate Accession MT184173, 1,538bp) concatenated sequences had 95.3% identity to F. lacertarum (NCBI accession: MH582185). The TUB2 region most closely aligns to F. equiseti, which is likely due to an absence of TUB2 sequences labeled for F. lacertarum in the NCBI database. Pathogenicity was confirmed by spray-inoculating Southern Harvest 80G4 sorghum panicles (n = 9) at anthesis with four ml of conidial suspension (3.3×104 conidia/ml). Control plants (n = 9) were sprayed with sterile water. Plastic bags were placed around panicles for 24 hours to ensure moist conditions during the infection period. Plants were maintained in a greenhouse under a 12-hour light cycle and fertilized bi-weekly with 20-20-20 fertilizer. Symptoms were observed on inoculated panicles after 14 days, and the F. lacertarum isolate was recovered from inoculated plants and confirmed using methods described above. Fusarium spp. were not re-isolated from non-inoculated control plants. Members of FIESC are known to contribute to the Fusarium Head Blight disease complex and may be capable of producing mycotoxins associated with infections (Lincy et al. 2011; Marin et al. 2012; Moretti 2017); however, mycotoxin characterization in F. lacertarum has not been characterized. To our knowledge, this is the first report of F. lacertarum causing disease to sorghum in North Carolina and the United States. Fusarium lacertarum may cause impactful losses to sorghum producers due to direct yield and quality losses by the pathogen as well as the potential for mycotoxins to impact trade.

Rancang Bangun Miniatur Alat Perontok Sorgum

The purpose of this research is to make it easier for farmers to harvest sorghum and avoid crop failure due to lack of supporting technology to produce ready-to-sell sorghum seeds. The method used in the process of designing the machine with the engineering process, namely the activity of designing a sorghum thresher machine, a miniature sorghum thresher machine was successfully designed with the main components: Hopper, threshing unit, threshing room, driving motor, frame, seed outlet and sorghum panicle. And with a machine capacity capable of threshing 10kg/hour sorghum seeds with a rotation speed of 2800rpm.

Sorghum Panicle Detection and Counting Using Unmanned Aerial System Images and Deep Learning.

Machine learning and computer vision technologies based on high-resolution imagery acquired using unmanned aerial systems (UAS) provide a potential for accurate and efficient high-throughput plant phenotyping. In this study, we developed a sorghum panicle detection and counting pipeline using UAS images based on an integration of image segmentation and a convolutional neural networks (CNN) model. A UAS with an RGB camera was used to acquire images (2.7 mm resolution) at 10-m height in a research field with 120 small plots. A set of 1,000 images were randomly selected, and a mask was developed for each by manually delineating sorghum panicles. These images and their corresponding masks were randomly divided into 10 training datasets, each with a different number of images and masks, ranging from 100 to 1,000 with an interval of 100. A U-Net CNN model was built using these training datasets. The sorghum panicles were detected and counted by a predicted mask through the algorithm. The algorithm was implemented using Python with the Tensorflow library for the deep learning procedure and the OpenCV library for the process of sorghum panicle counting. Results showed the accuracy had a general increasing trend with the number of training images. The algorithm performed the best with 1,000 training images, with an accuracy of 95.5% and a root mean square error (RMSE) of 2.5. The results indicate that the integration of image segmentation and the U-Net CNN model is an accurate and robust method for sorghum panicle counting and offers an opportunity for enhanced sorghum breeding efficiency and accurate yield estimation.

Influence of variety endosperm type and seed moisture content on threshability and mechanical damage of sorghum seeds

Abstract. Sorghum is an important cereal crop and it is native in African tropical areas. It is a globally cultivated crop and the fifth most important cereal after maize, rice, wheat and barley. The objective of this study was to determine the effects of endosperm type, seed moisture content and threshing methods on percentage threshability and mechanical damage of sorghum seeds. Two varieties of sorghum (Kari-mtama 1 and Seredo) were grown in two diverse locations, Kiboko and Katumani. Variety Kari-mtama1 has hard (vitreous) endosperm while the other variety Seredo has soft (non-vitreous) endosperm. After harvesting, sorghum panicles were dried in the sun and oven. The panicles were threshed separately for each drying method at two moisture levels, namely 18-20% and 13-14%. The three threshing methods used included beating with wooden stick in tied sack, using wooden mortar and pestle as well as using threshing machine. The results showed that the vitreous endosperm variety had significantly high percentage threshability and significantly low mechanical damage than non-vitreous endosperm variety under both drying methods. There was a significant (p≤0.05) increase in threshability and decrease in mechanical damage when the seeds were threshed at 13-14% moisture content compared to 18-20%. For better seed quality, threshing machine is the best method to be used to avoid mechanical damage and seed quality deterioration. Drying methods used to dry seed panicles before threshing had no effects on threshability and mechanical damage. This implies that sorghum panicles can be dried either in the sun or oven without affecting threshability and mechanical damage at the two moisture levels and by using the three threshing methods.

Semantic Segmentation of Sorghum Using Hyperspectral Data Identifies Genetic Associations.

This study describes the evaluation of a range of approaches to semantic segmentation of hyperspectral images of sorghum plants, classifying each pixel as either nonplant or belonging to one of the three organ types (leaf, stalk, panicle). While many current methods for segmentation focus on separating plant pixels from background, organ-specific segmentation makes it feasible to measure a wider range of plant properties. Manually scored training data for a set of hyperspectral images collected from a sorghum association population was used to train and evaluate a set of supervised classification models. Many algorithms show acceptable accuracy for this classification task. Algorithms trained on sorghum data are able to accurately classify maize leaves and stalks, but fail to accurately classify maize reproductive organs which are not directly equivalent to sorghum panicles. Trait measurements extracted from semantic segmentation of sorghum organs can be used to identify both genes known to be controlling variation in a previously measured phenotypes (e.g., panicle size and plant height) as well as identify signals for genes controlling traits not previously quantified in this population (e.g., stalk/leaf ratio). Organ level semantic segmentation provides opportunities to identify genes controlling variation in a wide range of morphological phenotypes in sorghum, maize, and other related grain crops.

A Deep Learning Semantic Segmentation-Based Approach for Field-Level Sorghum Panicle Counting

Small unmanned aerial systems (UAS) have emerged as high-throughput platforms for the collection of high-resolution image data over large crop fields to support precision agriculture and plant breeding research. At the same time, the improved efficiency in image capture is leading to massive datasets, which pose analysis challenges in providing needed phenotypic data. To complement these high-throughput platforms, there is an increasing need in crop improvement to develop robust image analysis methods to analyze large amount of image data. Analysis approaches based on deep learning models are currently the most promising and show unparalleled performance in analyzing large image datasets. This study developed and applied an image analysis approach based on a SegNet deep learning semantic segmentation model to estimate sorghum panicles counts, which are critical phenotypic data in sorghum crop improvement, from UAS images over selected sorghum experimental plots. The SegNet model was trained to semantically segment UAS images into sorghum panicles, foliage and the exposed ground using 462, 250 × 250 labeled images, which was then applied to field orthomosaic to generate a field-level semantic segmentation. Individual panicle locations were obtained after post-processing the segmentation output to remove small objects and split merged panicles. A comparison between model panicle count estimates and manually digitized panicle locations in 60 randomly selected plots showed an overall detection accuracy of 94%. A per-plot panicle count comparison also showed high agreement between estimated and reference panicle counts (Spearman correlation ρ = 0.88, mean bias = 0.65). Misclassifications of panicles during the semantic segmentation step and mosaicking errors in the field orthomosaic contributed mainly to panicle detection errors. Overall, the approach based on deep learning semantic segmentation showed good promise and with a larger labeled dataset and extensive hyper-parameter tuning, should provide even more robust and effective characterization of sorghum panicle counts.

Insect fauna of pests and their natural enemies inhabiting sorghum-panicles in Egypt

Sorghum panicles offer a very rich microenvironment for many insect pest species and their natural enemies. Thirty arthropod species belonging to 28 families, pertaining to 9 orders were obtained from sorghum panicles planted in Sohag Governorate, Egypt, during the 3 successive seasons of 2016–2018. Out of these species were 14 pests, 16 predators, and 3 parasitoids. Lepidopteran and hemipteran pests were the most dominant species-infested sorghum-panicles during the mature stages of the panicles. Three microlepidopteran pests, the noctuid, Eublemma (Autoba) gayneri (Roth.); the pyralid, Cryptoblabes gnidiella Millière, and the cosmopterigid, Pyroderces simplex Walsingham, were recorded as major pest species infesting sorghum panicles in Sohag Governorate. The dipteran parasitoid species, Nemorilla floralis (Fallen) (Tachinidae) emerged from the pupae of the E. gayneri and C. gnidiella, while the hymenopteran parasitoid, Brachymeria aegyptiaca (Chalcididae) was obtained from the pupae of all the studied microlepidopteran pests. Spiders, coccinellids, and Orius spp. were the dominant predators collected form panicles. Post-harvest, larvae, and pupae of lepidopteran pests, especially P. simplex recorded (147, 96, and 79 larvae) and (47, 30, and 73 pupae)/10 panicles in 2016, 2017, and 2018 seasons, respectively.

Fertility of Pedicellate Spikelets in Sorghum Is Controlled by a Jasmonic Acid Regulatory Module.

As in other cereal crops, the panicles of sorghum (Sorghum bicolor (L.) Moench) comprise two types of floral spikelets (grass flowers). Only sessile spikelets (SSs) are capable of producing viable grains, whereas pedicellate spikelets (PSs) cease development after initiation and eventually abort. Consequently, grain number per panicle (GNP) is lower than the total number of flowers produced per panicle. The mechanism underlying this differential fertility is not well understood. To investigate this issue, we isolated a series of ethyl methane sulfonate (EMS)-induced multiseeded (msd) mutants that result in full spikelet fertility, effectively doubling GNP. Previously, we showed that MSD1 is a TCP (Teosinte branched/Cycloidea/PCF) transcription factor that regulates jasmonic acid (JA) biosynthesis, and ultimately floral sex organ development. Here, we show that MSD2 encodes a lipoxygenase (LOX) that catalyzes the first committed step of JA biosynthesis. Further, we demonstrate that MSD1 binds to the promoters of MSD2 and other JA pathway genes. Together, these results show that a JA-induced module regulates sorghum panicle development and spikelet fertility. The findings advance our understanding of inflorescence development and could lead to new strategies for increasing GNP and grain yield in sorghum and other cereal crops.

Reconstructing the Transcriptional Ontogeny of Maize and Sorghum Supports an Inverse Hourglass Model of Inflorescence Development.

Assembling meaningful comparisons between species is a major limitation in studying the evolution of organismal form. To understand development in maize and sorghum, closely related species with architecturally distinct inflorescences, we collected RNA-seq profiles encompassing inflorescence body-plan specification in both species. We reconstructed molecular ontogenies from 40 B73 maize tassels and 47 BTx623 sorghum panicles and separated them into transcriptional stages. To discover new markers of inflorescence development, we used random forest machine learning to determine stage by RNA-seq. We used two descriptions of transcriptional conservation to identify hourglass-like stages during inflorescence development. Despite a relatively short 12 million years since their last common ancestor, we found maize and sorghum inflorescences are most different during their hourglass-like stages of development, following an inverse-hourglass model of development. We discuss whether agricultural selection may account for the rapid divergence signatures in these species and the observed separation of evolutionary pressure and developmental reprogramming.

Management of sorghum grain mold through induced resistance mechanism using foliar spray of Pseudomonas fluorescens strains and non-conventional chemicals: a field study

The study involved in-vitro and field studies using Pseudomonas fluorescens strains and some non-conventional chemicals for sorghum grain mold management. The culture filtrate of P. fluorescens strains PF-1 and PF-2 inhibited spore germination (p < 0.001) and mycelial growth of grain mold fungi. Also, tested chemicals and commercial fungicides showed significant mycelial growth inhibition (p < 0.001). PF-1, PF-2, ZnSO4·7H2O, salicylic acid and propiconazole were potentially effective and hence were tested in field (by foliar spraying). Two set of experiments were conducted simultaneously using naturally and artificially (Fusarium challenged) induced infected sorghum panicles. The lowest infection (score 4.2) was recorded with propiconazole. Disease severity was well controlled by PF-1 and PF-2, compared to ZnSO4·7H2O and salicylic acid. Furthermore, disease control was validated by determining total phenolic, flavonoid content and differentially induced phenolic acids (HPLC analysis) in harvested grains. Additionally, results of biochemical-traits, compatibility studies and antibiotic sensitivity of strains were found to be favourable.

Effects of cultivars, nitrogen fertilizer rate and plant density on the yield and yield components of sorghum

The objective of the study was to evaluate the effect of cultivar, nitrogen fertilizer rate and plant density on the yield and yield components of sorghum planted under different environmental conditions. Field experiment was carried out during the 2016–17 and 2017–18 planting seasons at two locations of North West Province, South Africa. The experimental design was a split-split plot arrangement fitted into a randomized complete block design with four replicates. The main plot factors were high and low plant densities. The nitrogen fertilizer rates were 0, 100 and 150 kg/ha as the sub-plot factor. The sub-sub plot factors were two sorghum cultivars, PAN 8625 and PAN 8816. Parameters measured for yield were panicle length, panicle mass/ha, 1000-grain mass, biomass yield and grain yield. Cultivar, plant density and location had significant effect (P < 0.05) on sorghum panicle mass/ha, grain and biomass yield. Sorghum cultivar PAN 8625 produced a higher grain yield than PAN 8816. The differences observed in sorghum panicle mass/ha were attributed to dissimilarities in panicle weight and seed size among the cultivars planted. The study concluded that PAN 8625 was better than PAN 8816 in terms of yield production. Mafikeng is a good production location for sorghum.

Fourier Transform Mid-Infrared (FTIR) Spectroscopy to Identify Tannin Compounds in The Panicle of Sorghum Mutant Lines

G5 and G8 are two sorghum mutant lines projected as sorghum varieties for ruminant’s feed. However, most of the sorghum panicles contains phenolic compounds, especially in the form of condensed tannins. The objective of this research was to investigate the phenolic and tannin compounds from the panicles of two sorghum mutant lines. The second objective was to identify tannin content by Fourier Transform Mid-Infrared (FTIR) Spectroscopy in the sorghum panicles. The two mutant lines were compared with Pahat variety. This research was arranged into a randomized complete block design with two factors. The first factor was the sorghum variety/mutant lines, whereas the second factor was the generative phase of harvesting time (flowering, soft dough and hard dough). The results showed that the panicles of G5 sorghum contained the lowest total phenol compounds (P<0.01). The average mean of G5 and G8 mutant lines produced lower total tannin content (27.3 and 18.2%, respectively) than Pahat variety (P<0.01). Generally, total phenol and total tannin in all variety/mutant lines increased with the increasing maturity stage (P<0.01). All the spectra of the sorghum panicle samples in different harvesting age investigated shared certain special similarities. A band due to alkane medium (C-H) stretch was observed at 2937-2854 cm−1. At 1521-1514 and 1446-1408 cm−1, a band due to the C-C aromatic compounds was observed. Based on those findings, it can be concluded that G5 and G8 mutant lines produced less total phenol and total tannin contents than Pahat variety. Meanwhile, FTIR could be used to describe the profile of tannin compounds qualitatively.

Automated detection and measurement of individual sorghum panicles using density-based clustering of terrestrial lidar data

Plant breeders normally require an assessment of various plant traits to breed new crop varieties or improve processes in crop production. This assessment, generally referred to as plant phenotyping, often involves significant manual measurement, which creates a bottleneck in breeding programs. Using current high-density terrestrial laser scanning systems presents an opportunity to measure individual plants and organ-specific traits to support various crop improvement and agronomic initiatives. The main goal of this study was to assess the feasibility of applying terrestrial laser scanning (TLS) data for estimating counts and individual dimensions (panicle length, width, and height) of sorghum panicles – the flowering parts of sorghum plants that eventually yield seed when the plants mature. To achieve our goal, we developed and assessed a density-based clustering approach to derive the targeted information from TLS data. Estimated individual panicle data (panicle counts and individual panicle dimensions) from a random sample of 20 plots were compared with LiDAR-derived panicle data. Overall, panicles were detected (counted) with an overall accuracy of 89.3% with a 10.7% omission and 14.3% commission rate. Omission errors were caused mainly by poor point cloud sampling, while commission errors were driven by spectral similarities between panicle and other crop components such as dry foliage. Estimated panicle dimensions were highly correlated with reference LiDAR-derived panicle measurements (Panicle length: Pearson correlation (r) = 0.88, Root mean square error (RMSE) = 3.10 cm; panicle width: r = 0.79, RMSE = 1.67 cm; plant height: r = 1.00, RMSE = 0.80 cm). A plot-level comparison involving 43 plots was also carried out between estimated panicle data and panicle data derived from a sample of harvested panicles and showed moderate to high correlations between the two datasets (Panicle length: r = 0.79, RMSE = 2.48 cm; panicle width: r = 0.63, RMSE = 1.49 cm; plant height: r = 0.86, RMSE = 11.4 cm). The lower correlations with field data may be reflective of the impact of sampling rates, the compaction and dry down of panicles after harvest and experimental error in general. An analysis of the impact of simulated noise on estimates, showed that the developed method is moderately robust to lower noise levels (<30%) but its performance deteriorates at high levels showing the critical need for prior noise filtering. Overall, this study shows that TLS and similar point cloud data has the potential to expedite field-based plant phenotyping tasks.

Evaluation of panicle residue from broom sorghum as a feed ingredient in finishing diets for lambs

Sorghum panicle residue (SPR), a by-product of Sorghum vulgare, obtained in the manufacture of brooms and wisks, has potential as a partial substitute for grain in growing-finishing diets for feedlot lambs. Accordingly, 48 Pelibuey×Katahdin lambs (initial weight=16.2±4.3 kg) were used in an 84-d growth-performance trial to evaluate its comparative feeding value. Lambs were blocked by weight and assigned within weight groupings to 12 pens (4 lambs per pen). The SPR was finely ground before it was incorporated into the diet. The basal diet contained 60% whole grain sorghum (WGS; DM basis). Dietary treatments consisted in the replacement of WGS with 0, 50, or 100% SPR. Replacement of WGS with SPR decreased (linear effect, P=0.04) average daily gain (ADG), and tended to increase (linear effect, P=0.06) dry matter intake (DMI). Replacement of WGS with SPR decreased (linear effect, P<0.01) gain efficiency (ADG : DMI), and observed dietary net energy (NE), as well as hot carcass weight, dressing percentage, kidney–pelvic–heart fat, and back fat thickness (linear effect, P⩽.05) Other carcass characteristics and wholesale cuts as a percentage of cold carcass weight were not affected by dietary treatments. It is concluded that SPR is a palatable feed ingredient for inclusion in finishing diets for feedlot lambs. The comparative NE values for SPR are 1.50 and 0.91 Mcal/kg for maintenance and gain, respectively, 75% the NE value of WGS. These NE values reflect the greater fiber content of SPR. To the extent that dietary energy density limits energy intake (and hence daily weight gain), appropriate constraints on level of SPR incorporation is warranted.

Semiautomated Feature Extraction from RGB Images for Sorghum Panicle Architecture GWAS

Because structural variation in the inflorescence architecture of cereal crops can influence yield, it is of interest to identify the genes responsible for this variation. However, the manual collection of inflorescence phenotypes can be time consuming for the large populations needed to conduct genome-wide association studies (GWAS) and is difficult for multidimensional traits such as volume. A semiautomated phenotyping pipeline, TIM (Toolkit for Inflorescence Measurement), was developed and used to extract unidimensional and multidimensional features from images of 1,064 sorghum (Sorghum bicolor) panicles from 272 genotypes comprising a subset of the Sorghum Association Panel. GWAS detected 35 unique single-nucleotide polymorphisms associated with variation in inflorescence architecture. The accuracy of the TIM pipeline is supported by the fact that several of these trait-associated single-nucleotide polymorphisms (TASs) are located within chromosomal regions associated with similar traits in previously published quantitative trait locus and GWAS analyses of sorghum. Additionally, sorghum homologs of maize (Zea mays) and rice (Oryza sativa) genes known to affect inflorescence architecture are enriched in the vicinities of TASs. Finally, our TASs are enriched within genomic regions that exhibit high levels of divergence between converted tropical lines and cultivars, consistent with the hypothesis that these chromosomal intervals were targets of selection during modern breeding.

Learning from farmers to improve sorghum breeding objectives and adoption in Mali

ABSTRACT Many efforts have been made to improve sorghum [Sorghum bicolor (L.) Moench] varieties, but adoption of improved varieties remains low. Sorghum has diverse panicle architecture and grain qualities that vary within and between races, and utilization and adoption may depend on these traits. Recent efforts in West Africa to improve local guinea race germplasm as a base material have diversified potential options: there are breeding materials with a range of panicle types with increased grain number per panicle and a range of droopiness, as well as laxness and threshability. This study was designed to expand our understanding about sorghum grain and panicle traits that are important for farmers in the Sudan savanna zone of Mali. We combined a sorghum panicle sorting activity with qualitative interviews in Mande and Dioïla to understand farmers’ knowledge and preferences about sorghum characteristics. A total of 20 panicle sorting activities and 20 interviews were conducted with men and women sorghum producers. Based on their roles and responsibilities in sorghum production and processing, farmers associated specific panicle types, plant types, and grain traits with aspects of pest control, threshability, storage duration, and yield. Farmers preferred open panicles and droopy architecture for disease and pest control; hard grains for storage and appropriate ratios of flour and grits; high density of grain on the panicle for yield; and specific glume qualities for threshability. Breeding programs need to consider these regional preferences and gender roles to develop appropriate material and increase adoption of productive varieties.

Potential of Three Trap Crops in Managing Nezara viridula (Hemiptera: Pentatomidae) on Tomatoes in Florida.

The southern green stink bug, Nezara viridula (L.) (Hemiptera: Pentatomidae), is a serious insect pest of tomatoes in Florida. In this study, we examined the use of three species of trap crops to manage N. viridula in North Florida tomato crops in 2014 and 2015. We used striped sunflower (Helianthus annuus) (Asterales: Asteraceae) and wild game feed sorghum (Sorghum bicolor) (Poales: Poaceae) in both years, but different species of millet each year: browntop millet (Panicum ramosum) (Poales: Poaceae) in 2014 and pearl millet (Pennisetum glaucum) (Poales: Poaceae) in 2015. The number of stink bug adults collected from wild game feed sorghum exceeded the number from sunflower, and none were collected from either species of millet. Sorghum attracted a significantly higher number of adults than did striped sunflower; however, both sunflower and sorghum attracted the adults of N. viridula. Adults of the pest feed on the sorghum panicle and sunflower head (inflorescence). Although fewer stink bugs were found feeding on sunflower, the sunflower was found to be a good source of other natural enemies and pollinators and also attracted significantly greater numbers of the brown stink bug Euschistus servus (Say) (Hemiptera: Pentatomidae) (another pest of tomatoes). While this study demonstrated the effectiveness of sorghum, we recommend that sorghum be planted with another trap crop, preferably sunflower, for better preventive control of the southern green stink bug.

Bioethanol production from spent mushroom compost derived from chaff of millet and sorghum

BackgroundIn Uganda, the chaff remaining from threshed panicles of millet and sorghum is a low value, lignocellulose-rich agricultural by-product. Currently, it is used as a substrate for the cultivation of edible Oyster mushrooms (Pleurotus ostreatus). The aim of this study was to assess the potential to exploit the residual post-harvest compost for saccharification and fermentation to produce ethanol.ResultsSorghum and millet chaff-derived spent oyster mushroom composts minus large mycelium particles were assessed at small-scale and low substrate concentrations (5% w/v) for optimal severity hydrothermal pre-treatment, enzyme loading and fermentation with robust yeasts to produce ethanol. These conditions were then used as a basis for larger scale assessments with high substrate concentrations (30% w/v). Millet-based compost had a low cellulose content and, at a high substrate concentration, did not liquefy effectively. The ethanol yield was 63.9 g/kg dry matter (DM) of original material with a low concentration (19.6 g/L). Compost derived from sorghum chaff had a higher cellulose content and could be liquefied at high substrate concentration (30% w/v). This enabled selected furfural-resistant yeasts to produce ethanol at up to 186.9 g/kg DM of original material and a concentration of 45.8 g/L.ConclusionsSpent mushroom compost derived from sorghum chaff has the potential to be an industrially useful substrate for producing second-generation bioethanol. This might be improved further through fractionation and exploitation of hemicellulosic moieties, and possibly the exploitation of the mycelium-containing final residue for animal feed. However, spent compost derived from millet does not provide a suitably high concentration of ethanol to make it industrially attractive. Further research on the difficulty in quantitatively saccharifying cellulose from composted millet chaff and other similar substrates such as rice husk is required.