BLIGHTSIM: A New Potato Late Blight Model Simulating the Response of Phytophthora infestans to Diurnal Temperature and Humidity Fluctuations in Relation to Climate Change

Potato and sweetpotato breeding at the international potato center: approaches, outcomes and the way forward

Root and tuber crop breeding is at the front and center of CIP's science program, which seeks to develop and disseminate sustainable agri-food technologies, information and practices to serve objectives including poverty alleviation, income generation, food security and the sustainable use of natural resources. CIP was established in 1971 in Peru, which is part of potato's center of origin and diversity, with an initial mandate on potato and expanding to include sweetpotato in 1986. Potato and sweetpotato are among the top 10 most consumed food staples globally and provide some of the most affordable sources of energy and vital nutrients. Sweetpotato plays a key role in securing food for many households in Africa and South Asia, while potato is important worldwide. Both crops grow in a range of conditions with relatively few inputs and simple agronomic techniques. Potato is adapted to the cooler environments, while sweetpotato grows well in hot climates, and hence, the two crops complement each other. Germplasm enhancement (pre-breeding), the development of new varieties and building capacity for breeding and variety testing in changing climates with emphasis on adaptation, resistance, nutritional quality and resource-use efficiency are CIP's central activities with significant benefits to the poor. Investments in potato and sweetpotato breeding and allied disciplines at CIP have resulted in the release of many varieties some of which have had documented impact in the release countries. Partnership with diverse types of organizations has been key to the centers way of working toward improving livelihoods through crop production in the global South.

Development of a model for Colletotrichum diseases with calibration for phylogenetic clades on different host plants

Fungi in the genus Colletotrichum cause serious pre- and post-harvest losses to several agricultural crops worldwide. Through a systematic literature review, we retrieved the published information on Colletotrichum anthracnose diseases on different host plants and developed a mechanistic model incorporating the main stages of the pathogen's life cycle and the effect of weather. The model predicts anthracnose progress during the growing season on the aerial organs of different crops, and was parameterized for seven Colletotrichum clades (acutatum, dematium, destructivum, gloeosporioides, graminicola, and orbiculare) and the singleton species, C. coccodes. The model was evaluated for the anthracnose diseases caused by fungi belonging to five clades on six hosts by using data from 17 epidemics that occurred in Italy, the USA, Canada, and Japan. A comparison of observed versus predicted data showed a concordance correlation coefficient of 0.928 and an average distance between real data and the fitted line of 0.044. After further validation, the model could be used to support decision-making for crop protection.

Climate change and plant pathogens

Global food security is threatened by climate change, both directly through responses of crop physiology and productivity, and indirectly through responses of plant-associated microbiota, including plant pathogens. While the interactions between host plants, pathogens and environmental drivers can be complex, recent research is beginning to indicate certain overall patterns in how plant diseases will affect crop production in future. Here, we review the results of three methodological approaches: large-scale observational studies, process-based disease models and experimental comparisons of pathosystems under current and future conditions. We find that observational studies have tended to identify rising temperatures as the primary driver of disease impact. Process-based models suggest that rising temperatures will lead to latitudinal shifts in disease pressure, but drying conditions could mitigate disease risk. Experimental studies suggest that rising atmospheric CO₂ will exacerbate disease impacts. Plant diseases may therefore counteract any crop yield increases due to climate change.

Climate Change Effects on Pathogen Emergence: Artificial Intelligence to Translate Big Data for Mitigation

Plant pathology has developed a wide range of concepts and tools for improving plant disease management, including models for understanding and responding to new risks from climate change. Most of these tools can be improved using new advances in artificial intelligence (AI), such as machine learning to integrate massive data sets in predictive models. There is the potential to develop automated analyses of risk that alert decision-makers, from farm managers to national plant protection organizations, to the likely need for action and provide decision support for targeting responses. We review machine-learning applications in plant pathology and synthesize ideas for the next steps to make the most of these tools in digital agriculture. Global projects, such as the proposed global surveillance system for plant disease, will be strengthened by the integration of the wide range of new data, including data from tools like remote sensors, that are used to evaluate the risk ofplant disease. There is exciting potential for the use of AI to strengthen global capacity building as well, from image analysis for disease diagnostics and associated management recommendations on farmers' phones to future training methodologies for plant pathologists that are customized in real-time for management needs in response to the current risks. International cooperation in integrating data and models will help develop the most effective responses to new challenges from climate change.

Development and Validation of a Mechanistic, Weather-Based Model for Predicting Puccinia graminis f. sp. tritici Infections and Stem Rust Progress in Wheat

Stem rust (or black rust) of wheat, caused by Puccinia graminis f. sp. tritici (Pgt), is a re-emerging, major threat to wheat production worldwide. Here, we retrieved, analyzed, and synthetized the available information about Pgt to develop a mechanistic, weather-driven model for predicting stem rust epidemics caused by uredospores. The ability of the model to predict the first infections in a season was evaluated using field data collected in three wheat-growing areas of Italy (Emilia-Romagna, Apulia, and Sardinia) from 2016 to 2021. The model showed good accuracy, with a posterior probability to correctly predict infections of 0.78 and a probability that there was no infection when not predicted of 0.96. The model's ability to predict disease progress during the growing season was also evaluated by using published data obtained from trials in Minnesota, United States, in 1968, 1978, and 1979, and in Pennsylvania, United States, in 1986. Comparison of observed versus predicted data generated a concordance correlation coefficient of 0.96 and an average distance between real data and the fitted line of 0.09. The model could therefore be considered accurate and reliable for predicting epidemics of wheat stem rust and could be tested for its ability to support risk-based control of the disease.

The Application of Terahertz Time-Domain Spectroscopy to Identification of Potato Late Blight and Fusariosis

Fusarium and late blight (fungal diseases of cereals and potatoes) are among the main causes of crop loss worldwide. A key element of success in the fight against phytopathogens is the timely identification of infected plants and seeds. That is why the development of new methods for identifying phytopathogens is a priority for agriculture. The terahertz time-domain spectroscopy (THz-TDS) is a promising method for assessing the quality of materials. For the first time, we used THz-TDS for assessing the infection of seeds of cereals (oats, wheat and barley) with fusarium and potato tubers of different varieties (Nadezhda and Meteor) with late blight. We evaluated the refractive index, absorption coefficient and complex dielectric permittivity in healthy and infected plants. The presence of phytopathogens on seeds was confirmed by microscopy and PCR. It is shown, that Late blight significantly affected all the studied spectral characteristics. The nature of the changes depended on the variety of the analyzed plants and the localization of the analyzed tissue relative to the focus of infection. Fusarium also significantly affected all the studied spectral characteristics. It was found that THz-TDS method allows you to clearly establish the presence or absence of a phytopathogens, in the case of late blight, to assess the degree and depth of damage to plant tissues.

A Mechanistic Weather-Driven Model for Ascochyta rabiei Infection and Disease Development in Chickpea

Ascochyta blight caused by Ascochyta rabiei is an important disease of chickpea. By using systems analysis, we retrieved and analyzed the published information on A. rabiei to develop a mechanistic, weather-driven model for the prediction of Ascochyta blight epidemics. The ability of the model to predict primary infections was evaluated using published data obtained from trials conducted in Washington (USA) in 2004 and 2005, Israel in 1996 and 1998, and Spain from 1988 to 1992. The model showed good accuracy and specificity in predicting primary infections. The probability of correctly predicting infections was 0.838 and the probability that there was no infection when not predicted was 0.776. The model's ability to predict disease progress during the growing season was also evaluated by using data collected in Australia from 1996 to 1998 and in Southern Italy in 2019; a high concordance correlation coefficient (CCC = 0.947) between predicted and observed data was obtained, with an average distance between real and fitted data of root mean square error (RMSE) = 0.103, indicating that the model was reliable, accurate, and robust in predicting seasonal dynamics of Ascochyta blight epidemics. The model could help growers schedule fungicide treatments to control Ascochyta blight on chickpea.