Crop Diseases

Agriculture is the most important economic activity for the majority of Tanzanians. Currently, the agricultural sector contributes about 29.1% of the GDP, 65.5% of employment, 65% of raw materials to the industrial sector and 30% of export earnings.Maize is the main staple crop in Tanzania. The average annual national maize consumption is estimated at over 3m metric tons (MT) (USAID, 2017), while production in 2016 was 5.9m MT harvested from 4.0m ha with a productivity of 1.46 t/ha.
Keywords: Maize production in Tanzania

A survey was conducted in 2012 to assess diseases on major crops grown in Darolebu district, west Hararge zone. More than 15 sites and more than 20 crops were visited at research center and on farmers’ field. Eight diseases on sorghum, four on maize, and two on haricot bean, one on ground nut were recorded. The expected yield of cereal crops in this cropping season from the cultivated land is far below the national average. Some of the farmers responded that no improved technologies such as improved seeds, fertilizers, fungicides are available to increase their yield. From this study the major diseases on different crops are identified and there is a need to introduce improved crop production techniques with full package and practical integrated disease management program.

Rice is suffering from several fungal diseases among them brown spot caused by Bipolaris oryzae is important. Rice brown spot (BS) is a chronic disease that affects millions of hectares of rice. Chaetomium cochliodes proved to be a new antagonistic fungus against brown leaf spot of rice. BS is by far one the strongest yield reducers amongst rice diseases today. Analysis of brown-spot infected control plants suggested that C.miyabeanus represses plant photosynthetic processes and nitrate reduction in order to trigger premature senescence and cause disease. Biological products formulated from Ch cochliodes were tested to control brown leaf spot of rice. Host plant resistance to disease is an effective and economical way to manage BS. Spot formation has been analyzed mainly using mutants. Many mutants exhibiting spontaneous cell death have been reported in various plants, including Arabidopsis, maize, barley, and rice.Three rice genes, Spl7, Spl11, and Spl18, have been cloned and characterized. BS is conventionally perceived as a secondary problem that reflects rice crops that experience physiological stresses, e.g. drought and poor soil fertility, rather than a true infectious disease.

The history of tropical crops in the second half of the twentieth century is, in large part, a history of innovation. An analysis of this history of innovation allows us to glimpse the environmental history of these crops. Much of the innovation in this period was done to counter an unprecedented wave of crop diseases and pests. Recovering the history of these diseases, in turn, allows us to understand the increasingly fragile ecology of the main crops in Central America. This article analyzes these themes through a history of the coffee rust − Hemileia vastatrix− in Central America, with particular emphasis on Costa Rica.

False smut disease is caused by Ustilaginoidea virens (Cooke) Takahashi on rice. It has become a serious pathogen in almost all rice-growing areas in the world. The fungus over winters in soil by means of sclerotia and chlamydospores. Sclerotia produce ascospores, which are primary source of infection to rice plants, whereas secondary infection may come from airborne chlamydospores. a precise assessment method to evaluate the severity of the disease was developed. The 'yield representative' (YR) based on 'mean floret wt.' and 'filled grain %' was simulated for the precise disease severity assessment of rice false smut disease. Different fungicides or bio-control agents against false smut were applied at different times before heading on a susceptible rice variety. three dsRNA segments from the rice false smut fungus Ustilaginoidea virens,the causal agent of a serious disease in rice, with molecular size ranging from 1.3 to 5 Kb, were isolated and named as dsRNA-L, dsRNA-M, and dsRNA-S. To investigate population genetics, it is necessary to find markers that are polymorphic, such as multilocus DNA fingerprinting, random amplified polymorphic DNA (RAPD) or amplified fragment length polymorphism (AFLP).

Agriculture is the basic occupation of all Indians. Farmer is said to be man of nation. We consider this as our responsibility to explore this occupation and take it to a higher level from technology point of view. The basic purpose for developing this system is crop disease prediction using various data mining techniques. Our project describes a new approach to crop disease prediction which helps to prevent future economical losses. This project emphasizes on every single concept related to crop diseases. This is accomplished by building a web platform in which farmers can interact with expert, share their experiences and knowledge. This results in a dynamically-growing online survey, which ultimately helps in data collection that can be used to identify various crop diseases and helps to prevent them. This portal can be used for multiple purposes where agro based industries can use our data to launch their products as well as acquire feedbacks. Agricultural institutes can explore new patterns in crop diseases and use required technology to prevent them. This system will be helpful for students perceiving agriculture studies; they can collect the correct information from the appropriate source and in precise manner.

Macadamia (Macadamia integrifolia) is native to eastern Australia and produces an edible nut that is extensively cultivated in commercial orchards in several countries. Little is known about the diversity of fungi associated with diseases of macadamia inflorescences. A survey of fungi associated with the dry flower disease of macadamia detected several isolates of Neopestalotiopsis (Pestalotiopsidaceae, Sordariomycetes). Five new species of Neopestalotiopsis were identified based on molecular phylogenetic analyses of concatenated gene sequences of the internal transcribed spacer (ITS), β-tubulin (TUB), and the translation elongation factor 1-alpha (TEF1α). The new species are named Neopestalotiopsis drenthii, N. maddoxii, N. olumideae, N. vheenae, and N. zakeelii, and are described by molecular, morphological, and cultural characteristics. The ecology of the isolates and their pathogenic, saprophytic, or commensal ability were not determined.

Crop epidemics provide a portal into the global and transnational environmental history of commodities. The coffee rust epidemic, caused by the fungus Hemileia vastatrix, is one of the most serious diseases to have afflicted the global coffee industry. In the nineteenth century, it devastated the coffee plantations in the Old World. It sharply curtailed arabica coffee production in Africa, Asia, and the Pacific. This was one of the factors that allowed the Americas do dominate global coffee production in the twentieth century. The coffee rust epidemic was first detected in the Americas in the 1970s. The history of the rust epidemic in the Americas, and attempts to control it, shed light on two major paradigms that shaped the environmental history of coffee in the late twentieth century. The paradigm of technification, which dominated from the mid-20th century to the early 1990s; and the paradigm of sustainability, which dominated emerged in the mid-1980s and continues to the present.

Approximately 58% of Indian population is involved in agriculture directly or indirectly, which contributed about 19.9% to the GDP of India in 2020-2021 F.Y. According to a report published by ICAR (Indian Council of Agricultural Research) about 30-35% of annual crop yield are wasted because of pests and diseases which affects the income and livelihood of the farmers. With the advancement in deep learning and computer vision it is now possible to detect the plant disease effectively by observing the disease pattern of leaves of plants. Which will help farmers to classify the disease in their plant. In this study about 12500 images of healthy and infected plant leaves which are available in public domain were used to train deep learning model, which can classify the respected disease.

Background: In this study, we compared four models for predicting rice blast disease, two operational process-based models (Yoshino and Water Accounting Rice Model (WARM)) and two approaches based on machine learning algorithms (M5Rules and Recurrent Neural Networks (RNN)), the former inducing a rule-based model and the latter building a neural network. In situ telemetry is important to obtain quality in-field data for predictive models and this was a key aspect of the RICE-GUARD project on which this study is based. According to the authors, this is the first time process-based and machine learning modelling approaches for supporting plant disease management are compared. Results: Results clearly showed that the models succeeded in providing a warning of rice blast onset and presence, thus representing suitable solutions for preventive remedial actions targeting the mitigation of yield losses and the reduction of fungicide use. All methods gave significant "signals" durin...

Zymoseptoria tritici is the causal agent of one of the European Union's most devastating foliar diseases of wheat: Septoria tritici Blotch (STB). It is also a notable pathogen of wheat grown in temperate climates throughout the world. In this commentary, we highlight the importance of STB on wheat in the EU. To better understand STB, it is necessary to consider the host crop, the fungal pathogen and their shared environment. Here, we consider the fungus per se and its interaction with its host and then focus on a more agricultural overview of the impact STB on wheat. We consider the climatic and weather factors which influence its spread and severity, allude to the agricultural practices which may mitigate or enhance its impact on crop yields, and evaluate the economic importance of wheat as a food and animal feed crop in the UK and EU. Finally, we estimate the cost of STB disease to EU agriculture.

During the Cold War, coffee became a strategically important crop in the global contest between the United States and the Soviet Union. The economies of many US allies in Latin America depended upon coffee. In the Cold War context, then, the coffee leaf rust (Hemileia vastatrix) became a geopolitical problem. Coffee experts in Latin America, which produced most of the world's coffee, began to prepare for an outbreak. In the 1950s, they built a global network of coffee experts. This network was sustained by US-led Cold War programs that promoted technical collaboration across the Global South, such as Harry Truman's Point Four programs. We explore the network's growth and evolution through one of its central figures, the American plant pathologist Frederick L. Wellman. This network has survived the end of the Cold War and evolved to reflect the new geopolitical context.

Collections of crop genetic resources are a valuable source of new genetic variation for economically important traits, including resistance to crop diseases. New sources of useful crop traits are often identified through evaluation in field trials. The number of relevant accessions in genebank collections available to be evaluated is often substantially larger than the capacity of the evaluation project. The focused identification of germplasm strategy (FIGS) is an approach used to select subsets of germplasm from genetic resource collections in such a way as to maximize the likelihood of capturing a specific trait. This strategy uses a range of methods to link the expression of a specific trait (of a target crop) with the eco-geographic parameters of the original collection site. This study contributes to the development of the approach by which a FIGS subset could be assembled for biotic traits. We have evaluated trait-specific subset selection methods for two fungal crop diseases, namely stem rust (Puccinia graminis Pers.) in wheat (Triticum aestivum L. and Triticum turgidum L.) and net blotch (Pyrenophora teres Drechs.) in barley (Hordeum vulgare L.). The results indicate that the climate layers from freely available eco-geographic databases are well suited to model and predict the reaction in these crops to biotic stress traits. This result has the potential to improve the efficiency of field screening trials to find novel sources of economically valuable crop traits.