Fungicide efficacy on tar spot and yield of corn in the Midwestern United States

Tar spot of corn caused by Phyllachora maydis has recently led to significant yield losses in the eastern corn belt of the Midwestern United States. Foliar fungicides containing quinone outside inhibitors(QoI), demethylation inhibitors(DMI), and succinate dehydrogenase inhibitors(SDHI) are commonly used to manage foliar diseases in corn. To mitigate the losses from tar spot thirteen foliar fungicides containing single or multiple modes of action (MOA/FRAC groups) were applied at their recommended rates in a single application at the standard tassel/silk growth stage timing to evaluate their efficacy against tar spot in a total of eight field trials in Illinois, Indiana, Michigan, and Wisconsin during 2019 and 2020. The single MOA fungicides included either a QoI or DMI. The dual MOA fungicides included a DMI with either a QoI or SDHI, and fungicides containing three MOAs included a QoI, DMI, and SDHI. Tar spot severity estimated as the percentage of leaf area covered by P. maydis stroma of the non-treated control at dent growth stage ranged from 1.6 to 23.3% on the ear leaf. Averaged across eight field trials all foliar fungicide treatments reduced tar spot severity, but only prothioconazole+trifloxystrobin, mefentrifluconazole+pyraclostrobin+fluxapyroxad, and mefentrifluconazole+pyraclostrobin significantly increased yield over the non-treated control. When comparing fungicide treatments by the number of MOAs foliar fungicide products that had two or three MOAs decreased tar spot severity over not treating and products with one MOA. The fungicide group that contained all three MOAs significantly increased yield over not treating with a fungicide or using a single MOA.

Determination of the Antagonistic Effects of Some Rhizospheric Bacteria against Macrophomina phaseolina under In Vitro Conditions

Macrophomina phaseolina (Tassi) Goid. is a fungal pathogen causes charcoal rot disease (Sin: Rhizoctonia bataticola) and is responsible for significant yield losses in many plants. In our study, we aimed to evaluate the antagonistic ability of 39 different bacteria, isolated from the fields of sugar beet in 2019, against the pathogen Macrophomina phaseolina isolated from sugar beet, beans and chickpeas. Approximately 31% of the bacteria showed antibiosis effect against the pathogen. It was determined that the effectiveness level of Lelliottia amnigena, Bacillus atrophaeus, B.pumilus and B. cereus (7 isolates) was moderate to high against Macrophomina phaseolina. Bacillus atrophaeus (PTo15-1a) showed the highest efficacy of 80%, 72.94% and 82.35% against Macrophomina phaseolina of chickpea, bean and sugar beet respectively. Lelliottia amnigena (Pto 14-1b) was moderately effective (57.78%) against the chickpea isolate of the pathogen. It was observed that of the seven Bacillus cereus isolates used in the experiment, three isolates (Pto14-1a, Pto12-1b, Pto17-1b) were highly effective against the chickpea pathogen, two (Pto12-1b, Pto14-2b) against bean pathogen, and one (Pto15-1b) against sugar beet isolate. Results have shown varied level of antagonism by different test bacterial against different Macrophomina phaseolina isolates, while the highest level of antibiosis shown by Bacillus atrophaeus against all pathogenic isolates indicated that it can be a potential future bioagent in managing the disease.

Rice crop response to site variability in a multi-locational trial: A call for site specific management

Yield is a net expression of genotype (G) x environment (E) interactions including management. However, the segregation of 'E' into respective causes is seldom done while 'G' is a constant. Soil is a component of 'E' with imminent variability in attributes among multiple locations. Data on yield response of varieties to a set of treatments in different soils from multi-locational yield maximisation trial under All India Coordinated Rice Improvement Project were regularly gathered. A dataset pertaining to a trial conducted in Karaikal district of Puducherry Union Territory was analysed to ascertain the site-specific crop responses with inherent variability in soils. Rice varieties, ADT 46, BPT 5204 and CR 1009 were tested for responses at 17 sites with farmer fertiliser practices (FFP), regional recommended fertiliser dose (RDF) and software, 'Nutrient Expert®' (2016) (NE) derived fertiliser quantities. Analysis of variance showed that test sites explained 59.3% variability in yield. A multivariate technique, Factor Analysis extracted two factors, which are linear combinations of soil attributes those explained 76% of variance in soils. Factor scores classified soils into four groups, owing to variability in soil properties. Soil texture influenced yield significantly (across varieties and treatments) (R2 = 11.1%). Sites varied in excess duration in nursery ranging from 2 - 26 days. However, this excess duration reduced number of panicles m-2 only in CR 1009 (r = -0.328**). General linear model with sites and treatments as fixed factors, their interactions and panicles m-2 as covariate predicted better (R2 = 90.3%) with their significant contribution to the model. The order of R2 (%) was Sites (59.3) > Varieties (27.4) > Treatments (13.6%) in explaining variability in yield highlighting site-specific responses. Mean differences between ADT 46 and BPT 5204; BPT 5204 and CR 1009 were significant. Yield significantly changed across sites and treatments when fertiliser management shifted from non-specific (FFP) to site-specific NE based calculations through RDF (region specific). Results of this trial placed emphasis on soil test-based crop management to realise the uniform best, which clearly is site specific crop management.

Screening of blast resistance genes in rice breeding samples

Rice is one of the most widespread and cultivated crops in the world. It is necessary to increase the yield of crops or expand their sown areas to resolve a food security problem in Russia. Current impossibility of expanding rice cultivated areas in the Rostov region and the need to maintain and increase its yield require developing new disease-resistant varieties. Rice genotypes with multiple blast resistance genes avoid significant yield losses. Since pyramiding and selection of resistance genes in the same genotype through traditional selection methods are complicated, it is urgent to search for homozygous samples using marker-assisted selection methods. This study was aimed to identify Pi-1, Pi-2, Pi-33 and Pi-ta blast resistance genes in breeding rice samples by MAS-methods. The study used CTAB-method for DNA-isolation, PCR, electrophoresis on agarose and polyacrylamide gels. The resulting gels were stained in a solution of ethidium bromide and photographed in ultraviolet light. To control the presence of blast resistance genes the following parental cultivars were used: C104LAC for the Pi-1 and Pi-33 genes, C101-A-51 for the Pi-2 gene, IR36 for the Pi-ta gene; Novator and Boyarin as controls of non-functional alleles of all studied genes. The 446 selection samples of the seventh generation were analyzed. As a result of the research, 127 rice samples that combine 2 or 3 different blast resistance genes were identified. The Pi-2 and Pi-33 genes combination was identified in 43 samples (1128/1, 1149/3, 1171/2, 1177/3, 1177/4, 1186/4, et al.). Samples with three resistance genes are the most interesting for selection and further breeding. For developing new blast-resistant varieties, we recommend using rice samples with the following combinations of resistance genes Pi-1+Pi-2+Pi-33 (1197/1, 1226/2, 1271/1, 1272/2), Pi-1+Pi-2+Pi-ta (1197/4, 1304/2, 1304/3, 1482/3, 1482/4, 1486/1) and Pi-2+Pi-33+Pi-ta (1064/1, 1064/3, 1281/2, 1281/3, 1281/4, 1282/2, 1283/1, 1283/2, 1284/3).

Yield results obtained in maize crop following the foliar fertilization with new biostimulators based on keratin and collagen hydrolysates of animal origin

The aim of the paper is to show the reaction of maize crop to the foliar application of new biofertilizers, obtained from the residues left after the wool tanning process and which are based on protein hydrolysates of keratin and collagen. The tests have been performed in microplots (10 sqm) and macroplots (1000 sqm), in three repetitions, in southern Romania. All data obtained as a result of the 2020 autumn harvest have been centralized in tables and statistically processed, in order to establish the influence of each product, compared to the control. The bifactorial analyses show that, in climatic stress conditions (prolonged drought – Calarasi), very significant increases in yield have been obtained with K4 (2.5 and 5.0 l/ha), K5 (5.0 l/ha) and C (5.0 l/ha), with increases between 700 and 1300 kg/ha, ie up to +24%. In macroplots, subject to a moderate drought (Teleorman), product C (5.0 l/ha) brought a yield increase of 1537 kg/ha (+26%), noting that in this location almost all tested biostimulators offer very significant yield growth (except KC). Based on the 2020 yields, it was possible to identify which the foliar biostimulators that will continue to be tested in 2021.

Screening of Lentil Genotypes against Highly Aggressive Strain of Fusarium oxysporum f. sp. lentis

Lentil is one of the most nutritious pulse crops known as masur and grown as a rainfed crop throughout the world in winter season. It is rich in protein, starch, micronutrients and dietary fiber. In the global scenario, India ranked first in the area and second in the production with Bihar being one the major lentil growing state. The majority of the lentil crop is grown in Tal area of the state. As it is cultivated as a rainfed crop, it gets severely affected by several biotic and abiotic stresses. Among the biotic stresses, Fusaium wilt, caused by Fusarium oxysporum f. sp. lentis (Fol) is one of the major fungal diseases and remarkably causing severe crop damage from vegetative to reproductive stage producing significant yield reduction. Fol isolate exhibit great variability and aggressiveness based on agro- climatic conditions. AGLF-11 isolate of Fol collected from Tal area of Bihar was found to be highly aggressive based on previous studies. For this, 50 diverse genotypes were screened against this isolate under greenhouse condition, out of which 14 genotypes showed high susceptibility, 29 genotypes showed moderate susceptibility, 5 genotypes exhibited moderately resistance and only 2 genotypes (L 7920 and DPL 58) exhibited resistance reaction.

A Comprehensive Review of the Coffee Leaf Miner Leucoptera coffeella (Lepidoptera: Lyonetiidae)—A Major Pest for the Coffee Crop in Brazil and Others Neotropical Countries

The coffee leaf miner (CLM) Leucoptera coffeella moth is a major threat to coffee production. Insect damage is related to the feeding behavior of the larvae on the leaf. During the immature life stages, the insect feeds in the mesophyll triggering necrosis and causing loss of photosynthetic capacity, defoliation and significant yield loss to coffee crops. Chemical control is used to support the coffee production chain, though market requirements move toward conscious consumption claiming for more sustainable methods. In this overview, we discuss aspects about the CLM concerning biology, history, geographical distribution, economic impacts, and the most relevant control strategies in progress. Insights to develop an integrated approach for a safer and eco-friendly control of the CLM are discussed here, including bio-extracts, nanotechnology, pheromones, and tolerant cultivars.

Alternations in Biochemical Components in Chilli (Capsicum annuum L.) Plants Infected with Chilli Leaf Curl Viral Disease

Chilli (Capsicum annuum L.) is an important spice crop grown all over the world. Chilli leaf curl, a viral disease outbreak in almost every chilli grown area causing significant yield losses. An experiment was conducted to analyze the biochemical changes in infected and healthy plant of chilli. The chlorophyll a, b and total were highest in healthy leaves then infected leaves. Moreover, severely infected leaves have lower amounts of chlorophyll contents. Out of nine cultivars total phenol and tannin contents were found maximum in Gucchedar, POL-75 and Byddagi Kaddi in comparison to highly susceptible cultivars Musa Badi, Phuley Joyti and NPKT-2.

Using Explainable Artificial Intelligence to Improve Process Quality: Evidence from Semiconductor Manufacturing

We develop a data-driven decision model to improve process quality in manufacturing. A challenge for traditional methods in quality management is to handle high-dimensional and nonlinear manufacturing data. We address this challenge by adapting explainable artificial intelligence to the context of quality management. Specifically, we propose the use of nonlinear modeling with Shapley additive explanations to infer how a set of production parameters and the process quality of a manufacturing system are related. Thereby, we contribute a measure of process importance based on which manufacturers can prioritize processes for quality improvement. Grounded in quality management theory, our decision model selects improvement actions that target the sources of quality variation. The decision model is validated in a real-world application at a leading manufacturer of high-power semiconductors. Seeking to improve production yield, we apply our decision model to select improvement actions for a transistor chip product. We then conduct a field experiment to confirm the effectiveness of the improvement actions. Compared with the average yield in our sample, the experiment returns a reduction in yield loss of 21.7%. Furthermore, we report on results from a postexperimental rollout of the decision model, which also resulted in significant yield improvements. We demonstrate the operational value of explainable artificial intelligence by showing that critical drivers of process quality can go undiscovered by the use of traditional methods. This paper was accepted by Charles Corbett, operations management.

Weed Mapping in Vineyards Using RGB-D Perception

Weed management is one of the major challenges in viticulture, as long as weeds can cause significant yield losses and severe competition to the cultivations. In this direction, the development of an automated procedure for weed monitoring will provide useful data for understanding their management practices. In this work, a new image-based technique was developed in order to provide maps based on weeds’ height at the inter-row path of the vineyards. The developed algorithms were tested in many datasets from vineyards with different levels of weed development. The results show that the proposed technique gives promising results in various field conditions.