Daily bias-corrected weather data and daily simulated growth data of maize, millet, sorghum, and wheat in the changing climate of sub-Saharan Africa.

Crop models are the primary means by which agricultural scientists assess climate change impacts on crop production. Site-based and high-quality weather and climate data is essential for agronomically and physiologically sound crop simulations under historical and future climate scenarios. Here, we describe a bias-corrected dataset of daily agro-meteorological data for 109 reference weather stations distributed across key production areas of maize, millet, sorghum, and wheat in ten sub-Saharan African countries. The dataset leverages extensive ground observations from the Global Yield Gap Atlas (GYGA), an existing climate change projections dataset from the Inter-Sectoral Model Intercomparison Project (ISIMIP), and a calibrated crop simulation model (the WOrld FOod Studies -WOFOST). The weather data were bias-corrected using the delta method, which is widely used in climate change impact studies. The bias-corrected dataset encompasses daily values of maximum and minimum temperature, precipitation rate, and global radiation obtained from five models participating in the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6), as well as simulated daily growth variables for the four crops. The data covers three periods: historical (1995-2014), 2030 (2020-2039), and 2050 (2040-2059). The simulation of daily growth dynamics for maize, millet, sorghum, and wheat growth was performed using the daily weather data and the WOFOST crop model, under potential and water-limited potential conditions. The crop simulation outputs were evaluated using national agronomic expertise. The presented datasets, including the weather dataset and daily simulated crop growth outputs, hold substantial potential for further use in the investigation of future climate change impacts in sub-Saharan Africa. The daily weather data can be used as an input into other modelling frameworks for crops or other sectors (e.g., hydrology). The weather and crop growth data can provide key insights about agro-meteorological conditions and water-limited crop output to inform adaptation priorities and benchmark (gridded) crop simulations. Finally, the weather and simulated growth data can also be used for training machine learning techniques for extrapolation purposes.

Characterizing patterns of seasonal drought stress for use in common bean breeding in East Africa under present and future climates.

Common bean (Phaseolus vulgaris L.) is the second most important source of dietary protein and the third most important source of calories in Africa, especially for the poor. In East Africa, drought is an important constraint to bean production. Therefore, breeding programs in East Africa have been trying to develop drought resistant varieties of common bean. To do this, breeders need information about seasonal drought stress patterns including their onset, intensity, and duration in the target area of the breeding program, so that they can mimic this pattern during field trials. Using the Decision Support for Agrotechnology Transfer (DSSAT) v4.7 model together with historical and future (Coupled Model Inter-comparison Project 6, CMIP6) climate data, this study categorized Ethiopia, Tanzania, and Uganda into different target population of environments (TPEs) based on historical and future seasonal drought stress patterns. We find that stress-free conditions generally dominate across the three countries under historical conditions (50-80% frequency). These conditions are projected to increase in frequency in Ethiopia by 2-10% but the converse is true for Tanzania (2-8% reduction) and Uganda (17-20% reduction) by 2050 depending on the Shared Socioeconomic Pathway (SSP). Accordingly, by 2050, terminal drought stresses of various intensities (moderate, severe, extreme) are prevalent in 34% of Uganda, around a quarter of Ethiopia, and 40% of the bean growing environments in Tanzania. The TPEs identified in each country serve as a basis for prioritizing breeding activities in national programs. However, to optimize resource use in international breeding programs to develop genotypes that are resilient to future projected stress patterns, we argue that common bean breeding programs should focus primarily on identifying genotypes with tolerance to severe terminal drought, with co-benefits in relation to adaptation to moderate and extreme terminal drought. Little to no emphasis on heat stress is warranted by 2050s.

Data-driven approaches can harness crop diversity to address heterogeneous needs for breeding products.

This perspective describes the opportunities and challenges of data-driven approaches for crop diversity management (genebanks and breeding) in the context of agricultural research for sustainable development in the Global South. Data-driven approaches build on larger volumes of data and flexible analyses that link different datasets across domains and disciplines. This can lead to more information-rich management of crop diversity, which can address the complex interactions between crop diversity, production environments, and socioeconomic heterogeneity and help to deliver more suitable portfolios of crop diversity to users with highly diverse demands. We describe recent efforts that illustrate the potential of data-driven approaches for crop diversity management. A continued investment in this area should fill remaining gaps and seize opportunities, including i) supporting genebanks to play a more active role in linking with farmers using data-driven approaches; ii) designing low-cost, appropriate technologies for phenotyping; iii) generating more and better gender and socioeconomic data; iv) designing information products to facilitate decision-making; and v) building more capacity in data science. Broad, well-coordinated policies and investments are needed to avoid fragmentation of such capacities and achieve coherence between domains and disciplines so that crop diversity management systems can become more effective in delivering benefits to farmers, consumers, and other users of crop diversity.

The Non-Coalescence of /h/ and Incomplete Neutralization in South Jeolla Korean.

This study examines articulatory and acoustic data in order to investigate the non-coalescence of /h/ in South Jeolla. Seoul Korean speakers produce /pap/ "rice" followed by /hana/ "one" as [pa.pha.na] with the coalescence of /p/ and /h/; this is called an aspiration merger. In South Jeolla Korean, this merger may be blocked, as in cases where speakers produce /pap+hana/ as [pa.ba.na]. Electroglottographic (EGG) data indicate the existence of two groups of South Jeolla speakers: one that merges the plosive and /h/ (the merger group), and the other with the canonical South Jeolla Korean pronunciation that does not merge the two consonants (the non-merger group). The production of non-coalesced lenis stops in the non-merger group is phonetically comparable with an underlying lenis stop produced by both of the groups. However, in the non-merger group, the open quotient (OQ) of a vowel following a non-coalesced lenis stop is higher (breathier) than that of an underlying lenis stop. Spectral tilt results display a similarly increased breathiness when the vowel follows a non-coalesced lenis stop. As for the non-merger group of South Jeolla, we argue that speakers display incomplete neutralization such that the non-merger group produces two types of voiced lenis stops differing in the phonation of the following vowel. These findings suggest that previous phonological analyses that posit the /h/-deletion in the non-merger group of South Jeolla Korean need to be revisited.

State of ex situ conservation of landrace groups of 25 major crops.

Crop landraces have unique local agroecological and societal functions and offer important genetic resources for plant breeding. Recognition of the value of landrace diversity and concern about its erosion on farms have led to sustained efforts to establish ex situ collections worldwide. The degree to which these efforts have succeeded in conserving landraces has not been comprehensively assessed. Here we modelled the potential distributions of eco-geographically distinguishable groups of landraces of 25 cereal, pulse and starchy root/tuber/fruit crops within their geographic regions of diversity. We then analysed the extent to which these landrace groups are represented in genebank collections, using geographic and ecological coverage metrics as a proxy for genetic diversity. We find that ex situ conservation of landrace groups is currently moderately comprehensive on average, with substantial variation among crops; a mean of 63% ± 12.6% of distributions is currently represented in genebanks. Breadfruit, bananas and plantains, lentils, common beans, chickpeas, barley and bread wheat landrace groups are among the most fully represented, whereas the largest conservation gaps persist for pearl millet, yams, finger millet, groundnut, potatoes and peas. Geographic regions prioritized for further collection of landrace groups for ex situ conservation include South Asia, the Mediterranean and West Asia, Mesoamerica, sub-Saharan Africa, the Andean mountains of South America and Central to East Asia. With further progress to fill these gaps, a high degree of representation of landrace group diversity in genebanks is feasible globally, thus fulfilling international targets for their ex situ conservation.

The determinants of common bean variety selection and diversification in Colombia.

Variety selection and diversification are climate change adaptation practices pursued by Colombian common bean producers. We investigate the drivers behind common bean variety selection and diversification in one of the most important common bean production regions in Colombia -Santander. The effects of climate change on this region are expected to be elevation driven. Exploiting the relationship between elevation-driven weather variations and climate change perception in Santander, we estimate an alternative-specific conditional logistic regression model to identify the determinants of common bean variety selection from a survey of producers. Using an ordered-logistic regression model, we also investigate the drivers behind common bean variety diversification within this farming community. We find that farms' elevation, household composition, and seed certification are some of the most important drivers behind farmers' common bean variety selection in Santander. We also find that varieties that sell at higher prices and have shorter vegetative cycles tend to be more preferred by farmers. Finally, farmers who receive more help from family members and own a tractor tend to grow more than one variety in the same production cycle. Common bean breeding programmes can exploit these drivers to design communication strategies to maximize uptake of newly developed common bean phenotypes.

One CGIAR and the Integrated Agri-food Systems Initiative: From short-termism to transformation of the world's food systems.

Agri-food systems are besieged by malnutrition, yield gaps, and climate vulnerability, but integrated, research-based responses in public policy, agricultural, value chains, and finance are constrained by short-termism and zero sum thinking. As they respond to current and emerging agri-food system challenges, decision makers need new tools that steer toward multi-sector, evidence-based collaboration. To support national agri-food system policy processes, the Integrated Agri-food System Initiative (IASI) methodology was developed and validated through case studies in Mexico and Colombia. This holistic, multi-sector methodology builds on diverse existing data resources and leverages situation analysis, modeled predictions, and scenarios to synchronize public and private action at the national level toward sustainable, equitable, and inclusive agri-food systems. Culminating in collectively agreed strategies and multi-partner tactical plans, the IASI methodology enabled a multi-level systems approach by mobilizing design thinking to foster mindset shifts and stakeholder consensus on sustainable and scalable innovations that respond to real-time dynamics in complex agri-food systems. To build capacity for these types of integrated, context-specific approaches, greater investment is needed in supportive international institutions that function as trusted in-region 'innovation brokers.' This paper calls for a structured global network to advance adaptation and evolution of essential tools like the IASI methodology in support of the One CGIAR mandate and in service of positive agri-food systems transformation.

Modeling growth, development and yield of cassava: A review.

Cassava is an important crop in the developing world. The goal of this study was to review published cassava models (18) for their capability to simulate storage root biomass and to categorize them into static and dynamic models. The majority (14) are dynamic and capture within season growth dynamics. Most (13) of the dynamic models consider environmental factors such as temperature, solar radiation, soil water and nutrient restrictions. More than half (10) have been calibrated for a distinct genotype. Only one of the four static models includes environmental variables. While the static regression models are useful to estimate final yield, their application is limited to the locations or varieties used for their development unless recalibrated for distinct conditions. Dynamic models simulate growth process and provide estimates of yield over time with, in most cases, no fixed maturity date. The dynamic models that simulate the detailed development of nodal units tend to be less accurate in determining final yield compared to the simpler dynamic and statistic models. However, they can be more safely applied to novel environmental conditions that can be explored in silico. Deficiencies in the current models are highlighted including suggestions on how they can be improved. None of the current dynamic cassava models adequately simulates the starch content of fresh cassava roots with almost all models based on dry biomass simulations. Further studies are necessary to develop a new module for existing cassava models to simulate cassava quality.

Understanding the complexity of disease-climate interactions for rice bacterial panicle blight under tropical conditions.

Bacterial panicle blight (BPB) caused by Burkholderia glumae is one of the main concerns for rice production in the Americas since bacterial infection can interfere with the grain-filling process and under severe conditions can result in high sterility. B. glumae has been detected in several rice-growing areas of Colombia and other countries of Central and Andean regions in Latin America, although evidence of its involvement in decreasing yield under these conditions is lacking. Analysis of different parameters in trials established in three rice-growing areas showed that, despite BPB presence, severity did not explain the sterility observed in fields. PCR tests for B. glumae confirmed low infection in all sites and genotypes, only 21.4% of the analyzed samples were positive for B. glumae. Climate parameters showed that Montería and Saldaña registered maximum temperature above 34°C, minimum temperature above 23°C, and Relative Humidity above 80%, conditions that favor the invasion model described for this pathogen in Asia. Our study found that in Colombia, minimum temperature above 23°C during 10 days after flowering is the condition that correlates with disease incidence. Therefore, this correlation, and the fact that Montería and Saldaña had a higher level of infected samples according to PCR tests, high minimum temperature, but not maximum temperature, seems to be determinant for B. glumae colonization under studied field conditions. This knowledge is a solid base line to design strategies for disease control, and is also a key element for breeders to develop strategies aimed to decrease the effect of B. glumae and high night-temperature on rice yield under tropical conditions.

Effects of task and language nativeness on the Lombard effect and on its onset and offset timing.

This study focuses on the differences in speech sound pressure levels (here, called speech loudness) of Lombard speech (i.e., speech produced in the presence of an energetic masker) associated with different tasks and language nativeness. Vocalizations were produced by native speakers of Japanese with normal hearing and limited English proficiency while performing four tasks: dialog, a competitive game (both communicative), soliloquy, and text passage reading (noncommunicative). Relative to the native language (L1), larger loudness increments were observed in the game and text reading when performed in the second language (L2). Communicative tasks yielded louder vocalizations and larger increments of speech loudness than did noncommunicative tasks regardless of the spoken language. The period in which speakers increased their loudness after the onset of the masker was about fourfold longer than the time in which they decreased their loudness after the offset of the masker. Results suggest that when relying on acoustic signals, speakers use similar vocalization strategies in L1 and L2, and these depend on the complexity of the task, the need for accurate pronunciation, and the presence of a listener. Results also suggest that speakers use different strategies depending on the onset or offset of an energetic masker.

Generating Artificial Reverberation via Genetic Algorithms for Real-Time Applications.

We introduce a Virtual Studio Technology (VST) 2 audio effect plugin that performs convolution reverb using synthetic Room Impulse Responses (RIRs) generated via a Genetic Algorithm (GA). The parameters of the plugin include some of those defined under the ISO 3382-1 standard (e.g., reverberation time, early decay time, and clarity), which are used to determine the fitness values of potential RIRs so that the user has some control over the shape of the resulting RIRs. In the GA, these RIRs are initially generated via a custom Gaussian noise method, and then evolve via truncation selection, random weighted average crossover, and mutation via Gaussian multiplication in order to produce RIRs that resemble real-world, recorded ones. Binaural Room Impulse Responses (BRIRs) can also be generated by assigning two different RIRs to the left and right stereo channels. With the proposed audio effect, new RIRs that represent virtual rooms, some of which may even be impossible to replicate in the physical world, can be generated and stored. Objective evaluation of the GA shows that contradictory combinations of parameter values will produce RIRs with low fitness. Additionally, through subjective evaluation, it was determined that RIRs generated by the GA were still perceptually distinguishable from similar real-world RIRs, but the perceptual differences were reduced when longer execution times were used for generating the RIRs or the unprocessed audio signals were comprised of only speech.

South India projected to be susceptible to high future groundnut failure rates for future climate change and geo-engineered scenarios.

With an increase in global mean temperature predicted for this century accompanied by more frequent extremes, will farming communities need to brace for increased crop failures and hardship? Solar dimming climate geoengineering has been proposed as a possible solution to combat rising global temperature but what effect will it or other climate related adaptation have on crop failures? We performed a crop modelling study using future climate and geoengineering projections to investigate these questions. Our results indicate that groundnut crop failure rates in Southern India are very sensitive to climate change, and project an increase of approximately a factor of two on average over this century, affecting one out of every two to three years instead of one in every five years. We also project that solar dimming geoengineering will have little impact on reducing these failure rates. In contrast, the projections for the rest of Indian regions show decreasing failure rates of 20-30%. In this research, we indicate why south India is more susceptible than the rest of the country and show that neither Solar dimming geoengineering nor reducing heat or water stress are able to fully counteract the increase in failure rates for this region. Thus our modelling projections indicate the potential for a grountnut crop failure crisis for the South India.

Importance of genetic parameters and uncertainty of MANIHOT, a new mechanistic cassava simulation model.

We identified the most sensitive genotype-specific parameters (GSPs) and their contribution to the uncertainty of the MANIHOT simulation model. We applied a global sensitivity and uncertainty analysis (GSUA) of the GSPs to the simulation outputs for the cassava development, growth, and yield in contrasting environments. We compared enhanced Sampling for Uniformity, a qualitative screening method new to crop simulation modeling, and Sobol, a quantitative, variance-based method. About 80% of the GSPs contributed to most of the variation in maximum leaf area index (LAI), yield, and aboveground biomass at harvest. Relative importance of the GSPs varied between warm and cool temperatures but did not differ between rainfed and no water limitation conditions. Interactions between GSPs explained 20% of the variance in simulated outputs. Overall, the most important GSPs were individual node weight, radiation use efficiency, and maximum individual leaf area. Base temperature for leaf development was more important for cool compared to warm temperatures. Parameter uncertainty had a substantial impact on model predictions in MANIHOT simulations, with the uncertainty 2-5 times larger for warm compared to cool temperatures. Identification of important GSPs provides an objective way to determine the processes of a simulation model that are critical versus those that have little relevance.

High-resolution and bias-corrected CMIP5 projections for climate change impact assessments.

Projections of climate change are available at coarse scales (70-400 km). But agricultural and species models typically require finer scale climate data to model climate change impacts. Here, we present a global database of future climates developed by applying the delta method -a method for climate model bias correction. We performed a technical evaluation of the bias-correction method using a 'perfect sibling' framework and show that it reduces climate model bias by 50-70%. The data include monthly maximum and minimum temperatures and monthly total precipitation, and a set of bioclimatic indices, and can be used for assessing impacts of climate change on agriculture and biodiversity. The data are publicly available in the World Data Center for Climate (WDCC; cera-www.dkrz.de), as well as in the CCAFS-Climate data portal (http://ccafs-climate.org). The database has been used up to date in more than 350 studies of ecosystem and agricultural impact assessment.

Check list of the Venezuelan millipedes species.

Here we provide a checklist of millipedes described or recorded in the literature from Venezuela. The diplopod fauna is comprised of eight orders, 18 families, 54 genera, and 157 species. The millipede orders Glomerida, Chordeumatida, Julida, Siphoniulida, and Platydesmida (known elsewhere in the Western Hemisphere) are not, as of yet, reported from the Venezuelan fauna. Two widely distributed invasive species, Asiomorpha coarctata and Oxidus gracilis, were recorded from Venezuela. All species records listed here contain comprehensive citation and synonymy lists. Numerous locality data are questionable and are discussed. For most species, the supposed deposition of the type specimens is given. However, the museum and gender information is taken mostly from the literature as the type specimen themselves were not examined. An analysis of records extracted from GBIF in 2016 and in 2018 was conducted and compared to the data captured from the published taxonomic literature. The data in this checklist are summarized online at the MilliBase website.

Utilizando un recurso inusual: escarabajos del estiércol atraídos a milpiés (Diplopoda: Spirobolida) / Using an unusual resource: dung beetles attracted to millipedes (Diplopoda: Spirobolida)

Entre 2017 y 2018 se realizaron muestreos durante cuatro meses en un fragmento de bosque tropical perennifolio de un área natural protegida de Chiapas, México. Se instalaron trampas de caída ubicados a lo largo de un trayecto de aproximadamente 500 m. Las trampas fueron cebadas con individuos recién muertos de Messicobolus magnificus, un milpiés abundante en la reserva durante la temporada lluviosa. Se capturaron 108 individuos de cuatro especies de escarabajos: Canthon vazquezae (92), Deltochilum pseudoparile (13), Coprophanaeus corythus (2) y Ateuchus rodriguezi (1). Únicamente D. pseudoparile ha sido previamente recolectada con carroña de otras especies de milpiés. El uso de este tipo de recurso posiblemente sea una fuente alterna que permite reducir la competencia interespecífica por otros recursos efímeros entre los Scarabaeinae.

Between 2017 and 2018, samplings out for four months in a tropical evergreen forest fragment from a natural protected area of Chiapas, Mexico, were carried. Pitfall traps were installed along a path of about 500 m. The traps were baited with freshly killed individuals of Messicobolus magnificus, an abundant millipede during the rainy season in the reserve. 108 individuals of four beetles' species were captured: Canthon vazquezae (92), Deltochilum pseudoparile (13), Coprophanaeus corythus (2) and Ateuchus rodriguezi (1). Only D. pseudoparile has been previously collected with carcasses from other millipede species. The use of this resource type is possibly an alternative source that reduces interspecific competition among the Scarabaeinae for other ephemeral resources.

Black Sigatoka in bananas: Ecoclimatic suitability and disease pressure assessments.

Black leaf streak disease, or black Sigatoka, is caused by the fungus Pseudocercospora fijiensis, and has been identified as a major constraint to global production of banana and plantain. We fitted a climatic niche model (CLIMEX) for P. fijiensis to gain an understanding of the patterns of climate suitability, and hence hazard from this disease. We then calibrated the climate suitability patterns against the results of an expert elicitation of disease pressure patterns. We found a moderately strong non-linear relationship between modelled climate suitability for P.°fijiensis and the expert ratings for disease pressure. The strength of the relationship provides a cross-validation between the CLIMEX model and the expert elicitation process. The bulk of global banana production experiences high potential threat from P. fijiensis, and the higher yielding areas for banana and plantain production are at greatest threat. By explicitly considering the role of irrigation we have been able to identify how strategic irrigation could be used to support banana production in areas that are at low risk from P. fijiensis.

Emergence of robust precipitation changes across crop production areas in the 21st century.

A warming climate will affect regional precipitation and hence food supply. However, only a few regions around the world are currently undergoing precipitation changes that can be attributed to climate change. Knowing when such changes are projected to emerge outside natural variability-the time of emergence (TOE)-is critical for taking effective adaptation measures. Using ensemble climate projections, we determine the TOE of regional precipitation changes globally and in particular for the growing areas of four major crops. We find relatively early (<2040) emergence of precipitation trends for all four crops. Reduced (increased) precipitation trends encompass 1-14% (3-31%) of global production of maize, wheat, rice, and soybean. Comparing results for RCP8.5 and RCP2.6 clearly shows that emissions compatible with the Paris Agreement result in far less cropped land experiencing novel climates. However, the existence of a TOE, even under the lowest emission scenario, and a small probability for early emergence emphasize the urgent need for adaptation measures. We also show how both the urgency of adaptation and the extent of mitigation vary geographically.