Banana production landscapes in the African Great Lakes Region (AGLR) have been under immense pressure from Xanthomonas wilt (XW) disease over the past two decades. XW, first reported on banana in central Uganda and eastern DR Congo in 2001, has since spread to the entire AGLR. XW is currently spreading westwards from hot spots in eastern DR Congo highlands, putting the plantain ( Musa AAB genome) belt of central and west Africa at risk. In-depth understanding of the key variables responsible for disease spread, current hotspots, and vulnerable landscapes is crucial for disease early warning and management. We mapped aggregated disease distribution and hotspots in the AGLR and identified vulnerable landscapes across African banana production zones. Available data on disease prevalence collected over 11 years was regressed against environmental and expert developed covariates to develop the AGLR XW hotspots map. For the Africa-wide risk map, precipitation, distance to hotspots, degree of trade in fresh banana products, production zone interconnectedness and banana genotype composition were used as covariates. In the AGLR, XW was mainly correlated to precipitation and disease/banana management. Altitude and temperature had unexpectedly low effects, possibly due to an overriding impact of tool-mediated spread which is part of the management covariate. In the AGLR, the eastern part of DR Congo was a large hotspot with highest vulnerability. Apart from endemic zones in the AGLR and Ethiopia, northern Mozambique was perceived as a moderate risk zone mainly due to the predominance of ‘Bluggoe’ ( Musa ABB type) which is highly susceptible to insect-vectored transmission. Presence of XW hotspots (e.g. eastern DR Congo) and vulnerable areas with low (e.g. north-western Tanzania) or no disease (e.g. Congo basin, western DR Congo and northern Mozambique) pressure suggest key areas where proactive measures e.g. quarantines and information sharing on XW diagnosis, epidemiology, and control could be beneficial.

Crop varieties should fulfill multiple requirements, including agronomic performance and product quality. Variety evaluations depend on data generated from field trials and sensory analyses, performed with different levels of participation from farmers and consumers. Such multi-faceted variety evaluation is expensive and time-consuming; hence, any use of these data should be optimized. Data synthesis can help to take advantage of existing and new data, combining data from different sources and combining it with expert knowledge to produce new information and understanding that supports decision-making. Data synthesis for crop variety evaluation can partly build on extant experiences and methods, but it also requires methodological innovation. We review the elements required to achieve data synthesis for crop variety evaluation, including (1) data types required for crop variety evaluation, (2) main challenges in data management and integration, (3) main global initiatives aiming to solve those challenges, (4) current statistical approaches to combine data for crop variety evaluation and (5) existing data synthesis methods used in evaluation of varieties to combine different datasets from multiple data sources. We conclude that currently available methods have the potential to overcome existing barriers to data synthesis and could set in motion a virtuous cycle that will encourage researchers to share data and collaborate on data-driven research.

The potential for bananas to produce year round is best expressed when water is abundant and daily temperatures are in the range of 20-30C. Zones with these conditions produce fruit for the global market. However, banana production, mainly for national markets, has developed in many subtropical areas under less than optimum conditions. Bananas are an important cash crop in southern Brazil, Paraguay and Argentina, in countries of North Africa, the Middle East and southern Africa, and in China and northern India. In these regions, bananas are subject to sub-optimum temperatures and short days. Highly favorable temperatures and long days in the summer may also include short periods of extreme temperatures above 35C, while rainfall is also highly variable. The effects of climate change on selected subtropical production areas were modeled in a two-step procedure using the EcoCrop model, under current growing conditions and for 2020 and 2050 using a set of 19 IPCC (Intergovernmental Panel on Climate Change) Global Climate Models (GCMs) under the SRES-A2 (business as usual) emission scenario. The modeling showed that current suitability for banana production in the subtropics is much lower than in the tropics with great variation in suitability within the subtropics. Of nine subtropical regions considered, two have improved conditions by 2020s, four are largely unaffected and three have a lower suitability. Our analysis also showed that, in terms of environmental conditions, certain sites are widely represented globally, offering options for technology transfer between sites. Other sites have few similar sites, which means that sites need to be carefully selected for approaches to technology development and transfer. The study leveraged site-specific information with widely available tools to understand potential effects of climate change in the subtropics. However, in order to fully understand the impacts of climate change on banana, the modeling tools used here need to be fully suited for semi-perennial crops to capture the effects of seasonal temperature and rainfall variability on crop cycle length and potential yields. INTRODUCTIONThe potential for bananas to produce year round is best expressed when water is abundant and daily temperatures are in the range of 20-30C (Simmonds, 1962). Numerous zones with

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