Brief description of the “Better Bean” objectives:

1. Downy mildew: identify genetic structures of field isolates, and improve disease prediction models.

The Problem: Currently, no markers exist for identifying current and emerging population of P. phaseoli in the field. Further and importantly, control of this disease suffers from outdated forecasting systems that do not take into account current environmental factors, as well as the prevailing field isolates’ abilities to tolerate higher temperatures (Races E and F).
The Strategy: 1.1 Differentiating pathogen populations in the field: We are using Genotyping-by-Sequencing: (GBS) on the P. phaseoli genome to identify differences among our 11 isolates, as well as new isolates we collect in coming years. This sub-objective also aims to grow P. phaseoli isolates more efficiently and axenically on defined media. 1.2 Improve disease forecasting models for downy mildew: We will develop and apply new methods for disease forecasting, based upon previously collected weather
data by Santamaria (2007). This sub-objective also looks at viability of oospores in the field over time, pre-emergence infection, and leaf wetness duration.

2. Pod blight: develop comprehensive monitoring and control strategies.

The Problem: P. capcisi causes pod blight on lima beans and is an emerging and potentially significant threat to growers. Managing this pathogen is difficult due to its wide host range, persistence in the soil, and ability to spread easily through irrigation water. Importantly, it has also manifested resistance to fungicides like mefanoxam. Together, these issues create an urgent need for understanding epidemiology on lima bean, fungicide use guidelines and prediction methods, and evaluation of biological controls.
The Strategy: 2.1: Assess the prevalence of P. capsici and develop risk based assessment guidelines for lima bean production fields: Sampling is beginning in 30 growers’ fields around Delaware; samples collected and ID’ed as P. capsici will be determined for mating type, fungicide resistance and genotype. Further, isolates will also be collected and characterized from irrigation water to obtain over 200 isolates from the Mid-Atlantic region, in total. 2.2: Evaluate the field efficacy of fungicides for P. capsici control and best use of fungicides and alternative control measures: Fungicide efficacy trials will begin in fall 2013 on a lima bean plot near Georgetown, DE.

3. White mold: develop comprehensive monitoring and control strategies.

The Problem: Currently, there are no specific guidelines for application of fungicides on lima beans against the white mold fungus, Sclerotinium sclerotiorum. Growers utilize guidelines for snap beans, but these are not directly applicable due to different responses of the two species to different fungicides. Lima bean production would be considerably enhanced by specific fungicide guidelines, as well as adoption of the
biocontrol fungus, Contans WG.

The Strategy: 3.1: Develop risk assessment guidelines for Delaware lima bean production fields and quantify the short and long term benefits of Contans WG: The research team will work with field personnel, consultants and growers to collect epidemiological data, white mold severity field histories, and flowering phenology of lima bean, as well as evaluate the impact of Contans WG on fungal ascospore dispersal. 3.2: Develop a fungicide disease response curve to provide growers with information on
the optimum time to spray for white mold under continuous disease pressure: To develop lima bean-specific spray guidelines, the fungicide Endura 70W will be applied to growers’ fields and several aspects including disease, yield, epidemiological factors, and fungal apothecial development, will be evaluated.

4. Root-knot nematode: develop methods for improved field assessment, control strategies, and identification of genetic resistance.

The Problem: Lima beans are plagued with root-knot nematodes throughout the growing season. Currently, sampling and identification measures are not adequate to predict coming infestations. And while biological controls and biofumigant crop and soil amendments have proven efficient at reducing RKN numbers in other crops, this is yet to be demonstrated for lima bean.

The Strategy: 4.1: Assess the prevalence of RKN in grower fields in the region, develop enhanced sampling strategies for RKN and develop predictive tools for RKN: To date, 8 fields with known histories of RKN infestation have been sampled and GPS referenced. Maps with nematode levels will be produced and uploaded to our website. 4.2: Evaluate alternative control measure for RKN in lima beans: Currently, two field trials are underway; the first involves soil amendments, while the second involves testing different biologicals and biorationals in microplots.

5. Genetic resource building: Characterize and develop germplasm resources to aid in the breeding  of new varieties.

The Problem: Knowledge of the genetic diversity and population structure of the lima bean, Phaseolus lunatus has never been evaluated. Understanding lima bean diversity will greatly enhance breeding efforts not only for pathogen/pest resistance, but also for abiotic stress.
The Strategy: 5.1: Assessing the structure of genetic diversity in lima bean: As per Objective 1, we will employ GBS to study 250 samples of lima bean germplasm, including
representatives from diverse geographic regions . 5.2: Development of a community population resource for genetic analysis of natural variation in lima bean: Results from 5.1 will guide the development of a genetically-defined mapping population made from highly diverse founder lines. Using nested association mapping (NAM), we will be able to dissect more complicated genetic traits important to lima bean production stability, such as yield and heat-tolerance.

6. Economic analysis: develop a long-term quantitative analysis of the economic impact of disease control technology adoption on lima bean growers’ profitability and loss due to pest pressure.

The Problem: To the best of our knowledge, a comprehensive economic analysis has neverbeen performed on lima bean production in the MAR. Production of this crop not only
generates food, it stabilizes the regional vegetable processing industry, providing many jobs.
The Strategy: We will perform an economic analysis based upon technology adoption of advances generated in Objectives 1 – 4, including grower adoption of new fungicide guidelinesand alternative control measures. We will utilize a dynamic panel estimation approach toanalyze the probability of the adoption of various control methods. Data will come from Objectives 1 – 4 themselves (experimental) as well as through growers’ responses to economic surveys. Surveys will be developed by the economic team, and the extension team.