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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Location‐specific information is required to support decision making in crop variety management, especially under increasingly challenging climate conditions. Data synthesis can aggregate data from individual trials to produce information that supports decision making in plant breeding programs, extension services, and of farmers. Data from on‐farm trials using the novel approach of triadic comparison of technologies (tricot) are increasingly available, from which more insights could be gained using a data synthesis approach. The objective of our study was to present the applicability of a rank‐based data synthesis approach to several datasets from tricot trials to generate location‐specific information supporting decision making in crop variety management. Our study focuses on tricot data from 14 trials of common bean (Phaseolus vulgaris L.) performed between 2015 and 2018 across four countries in Central America (Costa Rica, El Salvador, Honduras, and Nicaragua). The combined data of 17 common bean genotypes were rank aggregated and analyzed with the Plackett–Luce model. Model‐based recursive partitioning was used to assess the influence of spatially explicit environmental covariates on the performance of common bean genotypes. Location‐specific performance was predicted for the three main growing seasons in Central America. We demonstrate how the rank‐based data synthesis methodology allows integrating tricot trial data from heterogenous sources to provide location‐specific information to support decision making in crop variety management. Maps of genotype performance can support decision making in crop variety evaluation such as variety recommendations to farmers and variety release processes.

Synthesis of crop trial data can generate insights that are not available from the analysis of individual studies, but such synthesis is often constrained by the heterogeneity of data among studies. Rank‐based data synthesis provides the flexibility to combine data of heterogeneous types and from different sources. We demonstrate the application of rank‐based data synthesis of heterogeneous trial data to assess the effect of climatic factors on the reaction of several Musa genotypes to black leaf streak disease (BLSD; caused by Pseudocercospora fijiensis [sexual morph: Mycosphaerella fijiensis]). We aggregated data from the main public repositories of Musa trial data. We applied model‐based recursive partitioning with the Plackett‐Luce model, using climatic data as covariates. The model identified the maximum length of the dry spell as the main variable influencing differences in genotypic response to BLSD, dividing the aggregated trial dataset into humid and dry environments. We found differences in the reaction of genotypes to BLSD between these environments. In humid environments, NARITA 8 was found to be the most resistant genotype, while in dry environments FHIA‐01 was the best performing improved genotype. We also assessed reliability, which is the probability of outperforming the reference genotype (Calcutta 4). In humid environments NARITA 2, NARITA 8 and FHIA‐01 had the highest reliability, while in dry environments only the landrace Saba surpassed 50% reliability. The information generated by our data synthesis approach supports selecting Musa genotypes for further evaluations at new locations.This article is protected by copyright. All rights reserved

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